Imaging Of Structures Research Articles

Sparse sampling photoacoustic reconstruction with a graph regularization group sparse dictionary

Photoacoustic tomography (PAT) has emerged as a promising biomedical imaging technique. The combination of optical contrast and ultrasound spatial resolution in photoacoustic tomography overcomes the limitations of optical scattering, enabling clear imaging of tissue structures. However, achieving high-resolution photoacoustic images typically requires a large number of sensor detection elements for sufficient angular coverage. This demand for extensive data acquisition and processing raises concerns about efficiency and system complexity. While sparse sampling strategies can improve efficiency, preserving detailed structural information becomes challenging with a minimal number of detectors. To address the challenges of sparse sampling, compressed sensing (CS) techniques have been successfully applied for image reconstructions in 2D and 3D photoacoustic embodiments. In this context, we propose a joint graph regularization group sparse dictionary and total variational regularization (GRGS-TV) algorithm based on our previous work of a group sparse dictionary. It preserves structured information and geometric relationships among dictionary atoms. Moreover, TV regularization effectively preserves edge structures while exhibiting a certain degree of robustness and flexibility. Numerical simulations and in vivo experiments on mice validate the effectiveness of this method in improving photoacoustic image quality and suppressing artifacts. Comparative evaluations against other algorithms show enhanced performance in terms of image reconstruction evaluation indices. This innovative approach holds promise for advancing photoacoustic imaging in biomedical research and clinical diagnostics.

What Extra Information Can Be Provided by Multi-Component Seismic Data: A Case Study of 2D3C Prospecting of a Copper–Molybdenum Mine in Inner Mongolia, China

With the decrease in shallow mineral reserves, deep mineral resources have become the focus of exploration. Seismic exploration, renowned for its deep penetration and high spatial resolution and precision, stands as a primary technique in geophysical exploration. In comparison to traditional P-wave seismic exploration, multi-component seismic techniques offer the advantage of simultaneously acquiring P-wave and S-wave data, overcoming the limitations of single P-wave impedance in predicting lithology and enabling high-precision imaging of subsurface structures. Constrained by field survey costs, the reflection seismic illumination is lower and results in a poor signal-to-noise ratio of multi-component seismic data in metallic ore exploration, which poses great challenges in imaging converted S-waves. Based on the seismic and geological characteristics of metallic ores, this study conducts imaging research on metallic ore models through synthetic data and field multi-component seismic data from a copper–molybdenum mine in Inner Mongolia, China. The emphasis is given to PS-wave pre-stack time migration based on precisely sorting the commonly converted point so as to explore the feasibility and technical advantages of multi-component seismic exploration in metal mines. Synthetic data and field data testing demonstrate that PS-wave imaging contains more abundant structural and lithological information compared to PP-waves, indicating promising prospects for the application of multi-component seismic data in metallic ore exploration.

Image scanning microscopy using radially polarized beam with polarization conversion

Image scanning microscopy (ISM), which combines the confocal scanning microscopy (CSM) with fast wide-field CCD detection, has been demonstrated in the fluorescent imaging, achieving both high resolution and signal to noise ratio (SNR). In this paper, we introduce radially polarized beam to ISM for non-fluorescent imaging. Radially polarized beam is applied to obtain a sharper illumination point spread function (PSF), along with a polarization converter to enhance detection PSF. It is found that the lateral resolution of ISM using radially polarized beam with polarization conversion (RPC-ISM) is improved to 0.28λ by a factor of 20%, as compared to traditional CSM. Further, images of complex object structures are simulated based on the complete model of scanning microscopy. Simulation results indicate an enhanced transverse resolution in RPC-ISM, compared with conventional ISM using circularly polarized beam. This technique can be used for label-free imaging such as semiconductor inspection, nano-lithography and so on.

Advancing fluorescence imaging: enhanced control of cyanine dye-doped silica nanoparticles

BackgroundSilica nanoparticles (SNPs) have immense potential in biomedical research, particularly in drug delivery and imaging applications, owing to their stability and minimal interactions with biological entities such as tissues or cells.ResultsWith synthesized and characterized cyanine-dye-doped fluorescent SNPs (CSNPs) using cyanine 3.5, 5.5, and 7 (Cy3.5, Cy5.5, and Cy7). Through systematic analysis, we discerned variations in the surface charge and fluorescence properties of the nanoparticles contingent on the encapsulated dye-(3-aminopropyl)triethoxysilane conjugate, while their size and shape remained constant. The fluorescence emission spectra exhibited a redshift correlated with increasing dye concentration, which was attributed to cascade energy transfer and self-quenching effects. Additionally, the fluorescence signal intensity showed a linear relationship with the particle concentration, particularly at lower dye equivalents, indicating a robust performance suitable for imaging applications. In vitro assessments revealed negligible cytotoxicity and efficient cellular uptake of the nanoparticles, enabling long-term tracking and imaging. Validation through in vivo imaging in mice underscored the versatility and efficacy of CSNPs, showing single-switching imaging capabilities and linear signal enhancement within subcutaneous tissue environment.ConclusionsThis study provides valuable insights for designing fluorescence imaging and optimizing nanoparticle-based applications in biomedical research, with potential implications for targeted drug delivery and in vivo imaging of tissue structures and organs.Graphical

Advanced Image Stitching Method for Dual-Sensor Inspection.

Efficient image stitching plays a vital role in the Non-Destructive Evaluation (NDE) of infrastructures. An essential challenge in the NDE of infrastructures is precisely visualizing defects within large structures. The existing literature predominantly relies on high-resolution close-distance images to detect surface or subsurface defects. While the automatic detection of all defect types represents a significant advancement, understanding the location and continuity of defects is imperative. It is worth noting that some defects may be too small to capture from a considerable distance. Consequently, multiple image sequences are captured and processed using image stitching techniques. Additionally, visible and infrared data fusion strategies prove essential for acquiring comprehensive information to detect defects across vast structures. Hence, there is a need for an effective image stitching method appropriate for infrared and visible images of structures and industrial assets, facilitating enhanced visualization and automated inspection for structural maintenance. This paper proposes an advanced image stitching method appropriate for dual-sensor inspections. The proposed image stitching technique employs self-supervised feature detection to enhance the quality and quantity of feature detection. Subsequently, a graph neural network is employed for robust feature matching. Ultimately, the proposed method results in image stitching that effectively eliminates perspective distortion in both infrared and visible images, a prerequisite for subsequent multi-modal fusion strategies. Our results substantially enhance the visualization capabilities for infrastructure inspection. Comparative analysis with popular state-of-the-art methods confirms the effectiveness of the proposed approach.

Macular OCT's Proficiency in Identifying Retrochiasmal Visual Pathway Lesions in Multiple Sclerosis-A Pilot Study.

Optical coherence tomography (OCT) is a non-invasive imaging technique based on the principle of low-coherence interferometry that captures detailed images of ocular structures. Multiple sclerosis (MS) is a neurodegenerative disease that can lead to damage of the optic nerve and retina, which can be depicted by OCT. The purpose of this pilot study is to determine whether macular OCT can be used as a biomarker in the detection of retrochiasmal lesions of the visual pathway in MS patients. We conducted a prospective study in which we included 52 MS patients and 27 healthy controls. All participants underwent brain MRI, visual field testing, and OCT evaluation of the thicknesses of the peripapillary retinal nerve fiber layer (pRNFL), macular ganglion cell layer (GCL), and macular inner plexiform layer (IPL). OCT measurements were adjusted for optic neuritis (ON). VF demonstrated poor capability to depict a retrochiasmal lesion identified by brain MRI (PPV 0.50). In conclusion, the OCT analysis of the macula appears to excel in identifying retrochiasmal MS lesions compared to VF changes. The alterations in the GCL and IPL demonstrate the most accurate detection of retrochiasmal visual pathway changes in MS patients.

Autonomous Image-Based Corrosion Detection in Steel Structures Using Deep Learning.

Steel structures are susceptible to corrosion due to their exposure to the environment. Currently used non-destructive techniques require inspector involvement. Inaccessibility of the defective part may lead to unnoticed corrosion, allowing the corrosion to propagate and cause catastrophic structural failure over time. Autonomous corrosion detection is essential for mitigating these problems. This study investigated the effect of the type of encoder-decoder neural network and the training strategy that works the best to automate the segmentation of corroded pixels in visual images. Models using pre-trained DesnseNet121 and EfficientNetB7 backbones yielded 96.78% and 98.5% average pixel-level accuracy, respectively. Deeper EffiecientNetB7 performed the worst, with only 33% true-positive values, which was 58% less than ResNet34 and the original UNet. ResNet 34 successfully classified the corroded pixels, with 2.98% false positives, whereas the original UNet predicted 8.24% of the non-corroded pixels as corroded when tested on a specific set of images exclusive to the investigated training dataset. Deep networks were found to be better for transfer learning than full training, and a smaller dataset could be one of the reasons for performance degradation. Both fully trained conventional UNet and ResNet34 models were tested on some external images of different steel structures with different colors and types of corrosion, with the ResNet 34 backbone outperforming conventional UNet.

Orthodontic System Modeled and Simulated with the Lingual Technique to Assess Tooth Forces.

CBCT (cone beam computed tomography) is an imaging investigation that provides three-dimensional (3D) images of craniofacial structures. The purpose of this study is to determine the mechanical behavior of an orthodontic system where the lingual treatment technique was used in a 25-year-old female patient from whom a set of CBCT scans was used. CBCT images were processed through software programs such as Invesalius, Geomagic, and Solid Works, to create models containing virtual solids. These models were then imported into Ansys Workbench 2019 R3 (a finite element method software program) for successive simulations to generate displacement maps, deformations, stress distributions, and diagrams. We observed that in the lingual technique, the lowest force occurring on the maxillary teeth is at 1.1, while the highest force appears at 2.3. In the mandible, the lowest force occurs at 4.6, and the highest force at 3.1. The values of the forces and the results of the finite element method can represent a basis for the innovation of new orthodontic springs and also of bracket elements. Thus, by using new technologies, orthodontic practice can be significantly improved for the benefit of patients. Other virtual methods and techniques can be used in future studies, including the application of virtual reality for orthodontic diagnosis.

Contribution to the hyaliodine fauna (Miridae: Deraeocorinae: Hyaliodini) of New Caledonia with a description of two new species and a checklist of New Caledonian plant bugs

Two new species of the Montagneria Akingbohungbe, 1978—M. barbarae sp. nov. and M. nataliae sp. nov.—are diagnosed and described. Photographic images of habitus and genital structures, as well as scanning electron micrographs of the selected structures of both species are provided. The gland openings located on the thorax are figured and discussed. New records of three hyaliodine species, Femurocoris spinosus Carvalho, 1977, Montagneria cuneatus (Distant, 1920), and M. nigroscutellatum (Distant, 1920) in New Caledonia are also presented. Moreover, a checklist of the Miridae species of New Caledonia is given.

Photon-counting detector CT - first experiences in the field of musculoskeletal radiology.

The introduction of photon-counting detector CT (PCD-CT) marks a remarkable leap in innovation in CT imaging. The new detector technology allows X-rays to be converted directly into an electrical signal without an intermediate step via a scintillation layer and allows the energy of individual photons to be measured. Initial data show high spatial resolution, complete elimination of electronic noise, and steady availability of spectral image data sets. In particular, the new technology shows promise with respect to the imaging of osseous structures. Recently, PCD-CT was implemented in the clinical routine. The aim of this review was to summarize recent studies and to show our first experiences with photon-counting detector technology in the field of musculoskeletal radiology.We performed a literature search using Medline and included a total of 90 articles and reviews that covered recent experimental and clinical experiences with the new technology.In this review, we focus on (1) spatial resolution and delineation of fine anatomic structures, (2) reduction of radiation dose, (3) electronic noise, (4) techniques for metal artifact reduction, and (5) possibilities of spectral imaging. This article provides insight into our first experiences with photon-counting detector technology and shows results and images from experimental and clinical studies. · This review summarizes recent experimental and clinical studies in the field of photon-counting detector CT and musculoskeletal radiology.. · The potential of photon-counting detector technology in the field of musculoskeletal radiology includes improved spatial resolution, reduction in radiation dose, metal artifact reduction, and spectral imaging.. · PCD-CT enables imaging at lower radiation doses while maintaining or even enhancing spatial resolution, crucial for reducing patient exposure, especially in repeated or prolonged imaging scenarios.. · It offers promising results in reducing metal artifacts commonly encountered in orthopedic or dental implants, enhancing the interpretability of adjacent structures in postoperative and follow-up imaging.. · With its ability to routinely acquire spectral data, PCD-CT scans allow for material classification, such as detecting urate crystals in suspected gout or visualizing bone marrow edema, potentially reducing reliance on MRI in certain cases.. Bette S, Risch F, Becker J et al. Photon-counting detector CT - first experiences in the field of musculoskeletal radiology. Fortschr Röntgenstr 2024; DOI 10.1055/a-2312-6914.

A scientometric analysis of drone-based structural health monitoring and new technologies

Critical global challenges, such as climate change and the insufficient availability of resources, mean that it is a pivotal time to make cities more intelligent, efficient, and sustainable in a drive towards a net-zero carbon future. This requires intelligent, interactive, and responsive structural health monitoring (SHM) to assure the longevity and safety of ageing infrastructure. Drones have the potential to revolutionise SHM. Drone-based SHM (as a potential fly-by technique) involves equipping drones with various sensors, or using inbuilt sensors, to capture data and images of structures from different angles and perspectives. The data is then processed and analysed to facilitate accurate assessment of the structure’s health and early diagnosis of damage. Although the use of fly-by is relatively new, the speedy advances in various technologies that could be integrated with it, such as computer vision with artificial intelligence, deep learning, and links to digital twins, put these systems on the verge of a potential breakthrough. This paper provides an overview of fly-by SHM technique using both scientometric and qualitative systematic literature review processes, in order to provide a distinct understanding of the state of the art of research. As an original contribution, our research identified four main clusters of research within the field of fly-by SHM: (1) the application of UAV-enabled vision-based monitoring; (2) the integration of drones, advanced sensor technologies, and artificial intelligence; (3) drone-based SHM integrating modal analysis, energy harvesting, and deep learning; and (4) automation and robotics in drone-based SHM. The paper highlights the integration of new technologies such as artificial intelligence, machine learning, and sensors with the fly-by technique for SHM, identifies the gaps in current fly-by SHM research, and suggests new directions for research.

Enhancing Mechanical Performance of High-Density Polyethylene at Different Environmental Conditions with Outstanding Foamability through In-Situ Rubber Nanofibrillation: Exploring the Impact of Interface Modification.

In this study, we utilized in situ nanofibrillation of thermoplastic polyester ether elastomer (TPEE) within a high-density polyethylene (HDPE) matrix to enhance the rheological properties, foamability, and mechanical characteristics of the HDPE nanocomposite at both room and subzero temperatures. Due to the inherent polarity differences between these two components, TPEE is thermodynamically incompatible with the nonpolar HDPE. To address this compatibility issue, we employed a compatibilizer, styrene/ethylene-butylene/styrene copolymer-grafted maleic anhydride (SEBS-g-MA), to reduce the interfacial tension between the two blend components. In the initial step, we prepared a 10% masterbatch of HDPE/TPEE with and without the compatibilizer using a twin-screw extruder. Subsequently, we processed the 10% masterbatch further through spun bonding to create fiber-in-fiber composites. Scanning electron microscopy (SEM) analysis revealed a significant reduction in the spherical size of HDPE/TPEE particles following the inclusion of SEBS-g-MA, as well as a much smaller TPEE nanofiber size (approximately 60-70 nm for 5% TPEE). Moreover, extensional rheological testing revealed a notable enhancement in extensional rheological properties, with strain-hardening behavior being more pronounced in the compatibilized nanofibrillar composites compared to the noncompatibilized ones. SEM images of the foam structures depicted substantial improvement in the foamability of HDPE in terms of the cell size and density following the nanofibrillation process and the use of the compatibilizer. Ultimately, the in situ rubber fibrillation and enhancement of HDPE and TPEE interface using a compatibilizer led to increasing the HDPE ductility at room and subzero temperatures while maintaining its stiffness.

Research on an SICM Scanning Image Resolution Enhancement Algorithm.

Scanning ion conductance microscopy (SICM) enables the non-invasive three-dimensional imaging of live cells and other structures in physiological environments. However, when imaging complex samples, SICM faces challenges such as having a low temporal resolution during slow scanning and a reduced signal-to-noise ratio during fast scanning, making it difficult to simultaneously improve both temporal and spatial resolution. To address these issues, this paper proposes an algorithm for enhancing image resolution under high-speed scanning. Firstly, scanning images are preprocessed using a median filtering algorithm to remove the salt-and-pepper noise generated during high-speed scanning. Next, the Canny edge detection algorithm is employed to extract the edges of the image targets. To avoid blurring the edges, the new edge-directed interpolation (NEDI) algorithm is then used to fill the edges, while non-edge areas are filled using bilinear interpolation, thereby enhancing the image resolution. Finally, the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) are used to analyze the imaging of articular chondrocytes. The results show that under a scanning speed of 480 nm/ms, the proposed algorithm improves the temporal resolution of imaging by 60% compared to traditional 2× resolution imaging, increases the peak signal-to-noise ratio of the scanning images by 7 dB, and achieves a structural similarity of 0.97. Therefore, the proposed algorithm effectively removes noise during high-speed scanning and improves the SICM scanning imaging resolution, thereby avoiding the reduction in temporal resolution when scanning larger resolution samples and effectively enhancing the performance of SICM scanning imaging.

Spontaneous-stimulated Raman co-localization dual-modal analysis approach for efficient identification of tumor cells

The study of highly heterogeneous tumor cells, especially acute myeloid leukemia (AML) cells, usually relies on invasive analytical methods such as morphology, immunology, cytogenetics, and molecular biology classification, which are complex and time-consuming to perform. Mortality is high if patients are not diagnosed in a timely manner, so rapid label-free analysis of gene expression and metabolites within single-cell substructures is extremely important for clinical diagnosis and treatment. As a label-free and non-destructive vibrational detection technique, spontaneous Raman scattering provides molecular information across the full spectrum of the cell but lacks rapid imaging localization capabilities. In contrast, stimulated Raman scattering (SRS) provides a high-speed, high-resolution imaging view that can offer real-time subcellular localization assistance for spontaneous Raman spectroscopic detection. In this paper, we combined multi-color SRS microscopy with spontaneous Raman to develop a co-localized Raman imaging and spectral detection system (CRIS) for high-speed chemical imaging and quantitative spectral analysis of subcellular structures. Combined with multivariate statistical analysis methods, CRIS efficiently differentiated AML from normal leukocytes with an accuracy of 98.1 % and revealed the differences in the composition of nuclei and cytoplasm of AML relative to normal leukocytes. Compared to conventional Raman spectroscopy blind sampling without imaging localization, CRIS increased the efficiency of single-cell detection by at least three times. In addition, using the same approach for further identification of AML subtypes M2 and M3, we demonstrated that intracytoplasmic differential expression of proteins is a marker for their rapid and accurate classifying. CRIS analysis methods are expected to pave the way for clinical translation of rapid tumor cell identification.

A One-Step Methodology for Identifying Concrete Pathologies Using Neural Networks—Using YOLO v8 and Dataset Review

Pathologies in concrete structures can be visually evidenced on the concrete surface, such as by fissures or cracks, fragmentation of part of the concrete, concrete efflorescence, corrosion stains on the concrete surface, or exposed steel bars, the latter two occurring in reinforced concrete. Therefore, these pathologies can be analyzed via the images of concrete structures. This article proposes a methodology for visually inspecting concrete structures using deep neural networks. This method makes it possible to speed up the detection task and increase its effectiveness by saving time in preparing the identifications to be analyzed and eliminating or reducing errors, such as those resulting from human errors caused by the execution of tedious, repetitive analysis tasks. The methodology was tested to analyze its accuracy. The neural network architecture used for detection was YOLO, versions 4 and 8, which was tested to analyze the gain with migration to a more recent version. The dataset for classification was Ozgnel, which was trained with YOLO version 8, and the detection dataset was CODEBRIM. The use of a dedicated classification dataset allows for a better-trained network for this function and results in the elimination of false positives in the detection stage. The classification achieved 99.65% accuracy.

СТРУКТУРНАЯ ОРГАНИЗАЦИЯ ГЛИПРОЛИНОВОЙ ГЕКСАПЕПТИДНОЙ МОЛЕКУЛЫ

Computer modeling based on the use of the method of theoretical conformational analysis and programs that allow obtaining a graphic image of the spatial structures of biomolecules was performed for a hexapeptide molecule – glyproline H-Phe-Gly-Gly-Phe-Gly-Pro-OH. Glyprolines are short peptides whose amino acid sequences contain glycine and proline residues. Their mechanisms of action are currently poorly understood. Using the method of molecular mechanics, the spatial structure and conformational possibilities of this hexapeptide molecule were determined. Its potential energy was estimated as the sum of non-valente, electrostatic, torsion interactions and the energy of hydrogen bonds. 9 low-energy structures were found for the glyproline hexapeptide, the values of the dihedral angles of the main and side chains of the amino acids included in the molecule. The energy of intra- and interresidual interactions was estimated. The calculation showed that the folded forms of the main chain are low-energy for the hexapeptide. In them, the side chains of amino acids phenylalanine and proline, which are conformationally rigid, carry out effective interactions with all parts of the hexapeptide molecule.

Role of MRI in Evaluation of Post-Traumatic Knee Injuries: A Prospective Study

INTRODUCTIONThe knee joint is the largest and most complex joint in the human body. Therefore it is often affected by trauma. Magnetic Resonance Imaging (MRI) is a non-invasive imaging modality that provides detailed images of the soft tissue structures of the knee joint. The objective of this study was to evaluate MRI findings in patients with post-traumatic knee joint injuries. MATERIAL AND METHODSThis prospective study was carried out using a 3.0 Tesla MRI (Magnetom Lumina) scanner in the Department of Radiodiagno- sis and Medical Imaging at Universal College of Medical Sciences & Teaching Hospital, Bhairahawa, Nepal, involving 128 patients with knee injuries, from September 2023 to February 2024. Data were analyzed using SPSS version 20. RESULTSIn this study of 128 participants, 57.81% were male and 42.19% were female, with a mean age of 38.60±13.05 years. The majority of MRI findings were medial meniscus tear (51.56%), followed by anterior cruciate ligament tear (46.09%), medial collateral ligament sprain (44.53%), lateral meniscus tear (39.84%), lateral collateral ligament sprain (33.59%), and posterior cruciate ligament tear (17.97%). Our study also revealed various grades of collateral ligament injuries, including the medial and lateral ligaments. There were 59.65% of cases with a grade I medial collateral ligament sprain, followed by grade II at 33.33% and grade III at 7.02%. The grade I and grade II lateral collateral ligament sprains were 62.79% and 37.21%, respectively. CONCLUSIONMRI is a non-invasive and valuable diagnostic modality that can provide an accurate diagnosis. It is often preferred over arthroscopy.

2D and 3D visualization of herbaceous plant–plant contact zones using high‐resolution X‐ray computed tomography (HRXCT)

Societal Impact StatementParasitic plants that deprive crops of water and nutrients are an increasingly concerning food security issue, affecting the livelihood of millions of subsistence, small‐ and mid‐scale farmers. An in‐depth understanding of parasite–host interactions is required to develop species‐specific and ecologically sustainable parasite management methods. The non‐invasive visualization of herbaceous contact zones, applicable to diverse parasite–host pathosystems presented in this study, brings methodological advance to the research of biotic interactions between crops and plant parasites belonging to the most devastating parasitic plant family (Orobanchaceae). This work also provides first insights into how the parasites' feeding organ displaces host tissue beyond the direct parasite–host interface.SummaryHigh‐resolution X‐ray computed tomography (HRXCT) enables sectioning‐free two‐dimensional imaging of biological structures and reconstruction of three‐dimensional objects. Although its application is common in many areas of biomedicine and despite its flexibility regarding resolution levels, the technology remains underutilized in the plant sciences. Here, we explored HRXCT for the study of parasitic plant–plant interactions by developing protocols to access soft‐tissue host–parasite contact zones at cell‐level resolution. We tested various sample preparation methods and contrast stains for their efficiency to improve the imaging of haustorium samples. In doing so, we achieved cellular resolution with the visible cellular organization of haustorial structures, especially of the vascular system. Fresh stained and dehydrated sample preparation of soft haustoria enables the highest spatial resolution with fine‐cellular discrimination of haustorium versus host cells. Application of cell‐level resolved HRXCT to five pathosystems: Alectra‐cowpea, Phelipanche‐tomato, Phtheirospermum‐tomato, Rhamphicarpa‐tomato, and Striga‐sorghum highlighted a life history‐specific organization and uncovered an as yet undescribed internal displacement of host tissue at parasite–host interfaces. Following image‐based training, our HRXCT approach could invoke AI‐based cell recognition for automated parasite cell–host cell differentiation. Superseding extensive microsectioning for 3D imaging, the newly established HRXCT protocol for 2D‐ and 3D‐visualization of herbaceous plant–plant contact zones and the first insights gained from it, is useful for mid‐throughput, comparative studies of parasitic plant–host interactions.

Multiline orthogonal scanning temporal focusing (mosTF) microscopy for scattering reduction in in vivo brain imaging

Temporal focusing two-photon microscopy has been utilized for high-resolution imaging of neuronal and synaptic structures across volumes spanning hundreds of microns in vivo. However, a limitation of temporal focusing is the rapid degradation of the signal-to-background ratio and resolution with increasing imaging depth. This degradation is due to scattered emission photons being widely distributed, resulting in a strong background. To overcome this challenge, we have developed multiline orthogonal scanning temporal focusing (mosTF) microscopy. mosTF captures a sequence of images at each scan location of the excitation line. A reconstruction algorithm then reassigns scattered photons back to their correct scan positions. We demonstrate the effectiveness of mosTF by acquiring neuronal images of mice in vivo. Our results show remarkable improvements in in vivo brain imaging with mosTF, while maintaining its speed advantage.

3D drift correction for super-resolution imaging with a single laser light.

Single-molecule localization microscopy (SMLM) enables three-dimensional (3D) super-resolution imaging of nanoscale structures within biological samples. However, prolonged acquisition introduces a drift between the sample and the imaging system, resulting in artifacts in the reconstructed super-resolution image. Here, we present a novel, to our knowledge, 3D drift correction method that utilizes both the reflected and scattered light from the sample. Our method employs the reflected light of a near-infrared (NIR) laser for focus stabilization while synchronously capturing speckle images to estimate the lateral drift. This approach combines high-precision active compensation in the axial direction with lateral post-processing compensation, achieving the abilities of 3D drift correction with a single laser light. Compared to the popular localization events-based cross correlation method, our approach is much more robust, especially for datasets with sparse localization points.