Non-destructive Imaging Research Articles

Discriminative non-destructive imaging of flip chips based on photoacoustic remote sensing microscopy with layered elasto-optic models.

Discriminative internal imaging for different chip layers can pinpoint the location of critical defect in the flip chips, yet existing methods face challenges in in-line imaging to identify defects or structures from the sub-surface within the silicon substrate and their underlying coating. To address these challenges, we develop and verify layered elasto-optic models for photoacoustic remote sensing microscopy (PARS) that distinguish structures from multi-layers within a single device for in-line flip-chip wafer inspection. A finite-difference time-domain algorithm based on transparent source (TS-FDTD) accurately predicts different initial slopes of PARS signals within the silicon-metal and the silicon-air models. The initial slopes of PARS signals are experimentally validated and utilized for discriminative non-destructive imaging of the interdigital electrode chips and silicon cracks within the same region of interest. PARS with layered elasto-optic models and non-contact fast scanning has the potential for in-line detection of defects from various layered structures with different refractive indices, offering an approach for discriminative non-destructive testing (NDT) of flip-chip and layered structures.

Rapid pH Value Detection in Secondary Fermentation of Maize Silage Using Hyperspectral Imaging

As pH is a key factor affecting the quality of maize silage, its accurate detection is essential to ensuring product quality. Although traditional methods for testing the pH of maize silage feed are widely used, the procedures are often complex and time-consuming and may damage the sample. This study presents a non-destructive hyperspectral imaging (HSI) technology that provides a more efficient and cost-effective method of monitoring pH by capturing the spectral information of samples and analyzing their chemical and physical properties rapidly and without contact. We applied four spectral preprocessing methods, among which the multiplicative scatter correction (MSC) preprocessing method yielded the best results. To minimize model redundancy and enhance predictive performance, we utilized six feature extraction methods for characteristic wavelength extraction, integrating these with partial least squares (PLS), non-linear support vector machine regression (SVR), and extreme learning machine (ELM) algorithms to construct a quantitative pH value prediction model. The results showed that the model based on the bootstrapping soft shrinkage (BOSS) feature wavelength extraction method outperformed the other feature extraction methods, selecting 20 pH value-related feature wavelengths from 256 bands and building a stable BOSS–ELM model with prediction set determination coefficient (RP2), root-mean-square error of prediction (RMSEP), and relative percentage deviation (RPD) values of 0.9241, 0.4372, and 3.6565, respectively. To further optimize the model for precisely predicting pH at each pixel in hyperspectral images, we employed three algorithms: the genetic algorithm (GA), whale optimization algorithm (WOA), and bald eagle search (BES). These algorithms optimized and compared the BOSS–ELM model to obtain the best model for predicting maize silage pH: the BOSS–BES–ELM model. This model achieved a determination coefficient (RP2) of 0.9598, an RMSEP of 0.3216, and an RPD of 5.1448. We generated a visualized distribution map of pH value variation in maize silage using the BOSS–BES–ELM model. This study provides strong technical support and a reference for the rapid, non-destructive detection of maize silage pH from an image, an advancement of great significance to ensuring the quality of maize silage.

Non-destructive Near-infrared Hyperspectral Imaging in Food Technologies with a Focus on Monitoring Seed Viability

Seed qualities including viability and germination significantly influence the quantity and the quality of harvest. Technological means to assess seed qualities and attributes of seed-derived food products are varied. This paper highlights the use of infrared hyperspectral imaging (NIR- HSI) in food quality control, authentication, safety, process monitoring, shelf-life prediction, ingredients analysis, allergens detection and food sorting and grading. It also shows a particular application of NIR-HSI for the monitoring of nutrient content of sprouts and germinated seeds for industrial processing of foods with high nutritional values. The paper further reviews the applications of NIR-HSI to predict seed viability and germination. The non-destructive, rapid, and high-throughput capability of NIR-HSI were demonstrated through research works combining the NIR-HSI technology with chemometrics tools to reach more than 90% prediction rate. These relatively high rates may depend on the storage conditions or the stringency of the artificial aging conditions applied to parts of the seeds. However, the NIR-HSI has also proven efficient using naturally aged seeds with the prediction rates up to 90% correct classification, demonstrating the high capability of the technology. In combination with advanced chemometrics tools, some components of emerging technologies such as traditional machine learning and deep learning models have been added to increase the efficiency of NIR_HSI. Overall, the research works reviewed in this paper and which cover several food crops and food products showed that NIR-HSI is set to reach new heights in monitoring seed viability for improved seed stock management, crop production and innovation in the food industry.

Alrecon: computed tomography reconstruction web application based on Solara

Synchrotron X-ray computed tomography is a non-destructive 3D imaging technique that offers the possibility to study the internal microstructure of samples with high spatial and temporal resolution. Given its unmatched image quality and acquisition speed, and the possibility to preserve the specimens, there is an increasing demand for this technique, from scientific users from innumerable disciplines. Computed tomography reconstruction is the computational process by which experimental radiographs are converted to a meaningful 3-dimensional image after the scan. The procedure involves pre-processing steps for image background and artifact correction on raw data, a reconstruction step approximating the inverse Radon-transform, and writing of the reconstructed volume image to disk. Several open-source Python packages exist to help scientists in the process of tomography reconstruction, by offering efficient implementations of reconstruction algorithms exploiting central or graphics processing unit (CPU and GPU, respectively), and by automating significant portions of the data processing pipeline. A further increase in productivity is attained by scheduling and parallelizing demanding reconstructions on high performance computing (HPC) clusters. Nevertheless, visual inspection and interactive selection of optimal reconstruction parameters remain crucial steps that are often performed in close interaction with the end-user of the data. As a result, the reconstruction task involves more than one software. Graphical user interfaces are provided to the user for fast inspection and optimization of reconstructions, while HPC resources are often accessed through scripts and command line interface. We propose Alrecon, a pure Python web application for tomographic reconstruction built using Solara. Alrecon offers users an intuitive and reactive environment for exploring data and customizing reconstruction pipelines. By leveraging upon popular 3D image visualization tools, and by providing a user-friendly interface for reconstruction scheduling on HPC resources, Alrecon guarantees productivity and efficient use of resources for any type of beamline user.

Integrating deep learning with non-destructive thermal imaging for precision guava ripeness determination.

To mitigate post-harvest losses and inform harvesting decisions at the same time as ensuring fruit quality, precise ripeness determination is essential. The complexity arises in assessing guava ripeness as a result of subtle alterations in some varieties during the ripening process, making visual assessment less reliable. The present study proposes a non-destructive method employing thermal imaging for guava ripeness assessment, involving obtaining thermal images of guava samples at different ripeness stages, followed by data pre-processing. Five deep learning models (AlexNet, Inception-v3, GoogLeNet, ResNet-50 and VGGNet-16) were applied, and their performances were systematically evaluated and compared. VGGNet-16 demonstrated outstanding performance, achieving average precision of 0.92, average sensitivity of 0.93, average specificity of 0.96, average F1-score of 0.92 and accuracy of 0.92 within a training duration of 484 s. The present study presents a scalable and non-destructive approach for guava ripeness determination, contributing to waste reduction and enhancing efficiency in supply chains and fruit production. These initiatives align with environmentally friendly practices in agriculture. © 2024 Society of Chemical Industry.

Three-dimensional super-resolution crack imaging in industrial manufactured components: A truncated correlation photothermal coherence tomography approach

The challenge of detecting cracks in powder metallurgy (PM) components has been a topic of interest for many years, with no practical commercial solution currently in place. This study employs enhanced truncation-correlation photothermal coherence tomography (eTC-PCT) to visualize 3D cracks within PM automotive parts. Through the application of effective diffusion-wave reversal techniques, blurred infrared thermophotonic images of subsurface defects (cracks) in (“green”) compressed metal PM components were restored to their original geometric resolution over the entire depth range. This approach enabled the creation of 3-dimensional depth-resolved photothermal tomographic images and cross-sectional mappings of cracks. The developed technique reveals the precise spatial dimensions of surface and subsurface cracks, reaching depths of down to 3 mm (not an upper limit) that conventional thermal imaging cannot access due to limitations imposed by the depth-integrated nature of conventional thermal-wave imaging, the properties of PM materials, and the physics of spreading diffusion. The super-resolution method was further applied to a sintered automotive part, specifically validating the efficacy of eTC-PCT for non-destructive imaging (NDI) in manufactured automotive components. Diffusion reversal imaging shows promise as a non-destructive testing (NDT) tool, with potential applications in a wide range of manufactured products, including both the green and sintered stages of automotive component production.

Enamel Caries Lesion Depth Obtained by Optical Coherence Tomography and Transverse Microradiography: A Comparative Study

Introduction: Visual imaging of subsurface caries lesions is of vital interest in dentistry, which can be obtained by invasive radiography technique as well as by available non-destructive imaging approaches. Thus, as a first step toward the development of a new innovative approach, Spectral-domain optical coherence tomography (SD-OCT) was applied to detect the lesion depth in comparison to the established reference technique (transverse microradiography [TMR]). Methods: Bovine enamel specimens were demineralized for 5 days, following previous studies. For OCT, the resulting artificial lesions were scanned three-dimensionally (SD-OCT) and semi-automated measured (CarLQuant). For TMR, specimens were sectioned and the lesion depth was manually determined (Inspektor Research System). Results: The range of lesion depth detected with OCT was 24.0–174.0 μm (mouth rinse study), 18.0–178.0 μm (toothpastes study) and with TMR 59.2–198.0 μm (mouth rinse study), 33.2–133.4 μm (toothpastes study). We found a strong correlation between both methods in terms of lesion depth (Spearman rankwith outlierp < 0.001, Rho = 0.75, Spearman rankwithout outlierp = 0.001, Rho = 0.79). The two methods produce similar results (Passing-Bablok regression, 1.16). As deeper is the lesion, the smallest is the difference between both methods as indicated by Bland-Altman-plots. Conclusion: Especially in the case of deep lesions, the values obtained by both methods are in agreement, and OCT can potentially substitute TMR to detect and assess lesion depth with the benefit of being non-destructive.

In vivo X-ray microtomography locally affects stem radial growth with no immediate physiological impact.

Microcomputed tomography (µCT) is a nondestructive X-ray imaging method used in plant physiology to visualize in situ plant tissues that enables assessments of embolized xylem vessels. Whereas evidence for X-ray-induced cellular damage has been reported, the impact on plant physiological processes such as carbon (C) uptake, transport, and use is unknown. Yet, these damages could be particularly relevant for studies that track embolism and C fluxes over time. We examined the physiological consequences of µCT scanning for xylem embolism over 3 mo by monitoring net photosynthesis (Anet), diameter growth, chlorophyll (Chl) concentration, and foliar nonstructural carbohydrate (NSC) content in 4 deciduous tree species: hedge maple (Acer campestre), ash (Fraxinus excelsior), European hornbeam (Carpinus betulus), and sessile oak (Quercus petraea). C transport from the canopy to the roots was also assessed through 13C labeling. Our results show that monthly X-ray application did not impact foliar Anet, Chl, NSC content, and C transport. Although X-ray effects did not vary between species, the most pronounced impact was observed in sessile oak, marked by stopped growth and stem deformations around the irradiated area. The absence of adverse impacts on plant physiology for all the tested treatments indicates that laboratory-based µCT systems can be used with different beam energy levels and doses without threatening the integrity of plant physiology within the range of tested parameters. However, the impacts of repetitive µCT on the stem radial growth at the irradiated zone leading to deformations in sessile oak might have lasting implications for studies tracking plant embolism in the longer-term.

Low temperature PECVD processes for the fabrication of integrated symmetrical resonant UV210 organic/semiconductor structures

The resonant structures used as sensors for investigating specific dark and opaque substances may pose difficulties due to their potential aggressiveness towards photonic chips and the risk of destruction upon contact. To avoid it, this study presents a detailed description of a novel symmetrical waveguide structure with UV210 enclosed between Si/SiO2 bi-layers implemented for integrated photonics. The fabrication process is thoroughly explained, accompanied by various analyses conducted for characterizations purposes. The structure is composed of an organic UV210 material developed using deep UV lithography at 248 nm and fabricated onto an oxidized silicon layer, resulting in a Si/SiO2 bi-layer configuration. This process enables the fabrication of µm-scale access waveguides and racetrack Micro-Resonators (MRs) with a 400-nanometers gap. Several multilayer families have been produced using various low-temperature PECVD processes to obtain symmetrical Si/SiO2/UV210/Si/SiO2 structures and then properly characterized using non-destructive analyses and imaging techniques. The advantage of structural symmetry is primarily related to the electromagnetism and guidance equations, which eliminate the requirement for a cut-off thickness (or frequency), enabling significant miniaturization. Additionally, the upper cladding composed of a Si/SiO2 bi-layer offers protection against potentially aggressive substances directly in contact with the organic waveguide core and MRs. Different bi-layer upper cladding families, produced under various PECVD conditions, were characterized using XPS to understand the bonding mechanism between the silicon and UV210 organic. In addition, ellipsometric analyses were conducted to determine the real and imaginary components of the refractive index of the global structure and the upper cladding bi-layer. Structure imaging were obtained by Raman analyses and optical statistical measurements were conducted on four different types of heterostructures, with silica temperatures ranging from 120°C to 150°C during the second PECVD process. The resonance analysis, performed through Free Spectral Range (FSR) measurements, allowed us to draw conclusions regarding the stability and reproducibility of the fabrication process, as well as the impact of PECVD processes conditions on the optical measurements. Such optoelectronic resonant elements and hybrid heterostructures enable the investigation of aggressive substances in direct contact.

Toward robust and high-throughput detection of seed defects in X-ray images via deep learning

BackgroundThe detection of internal defects in seeds via non-destructive imaging techniques is a topic of high interest to optimize the quality of seed lots. In this context, X-ray imaging is especially suited. Recent studies have shown the feasibility of defect detection via deep learning models in 3D tomography images. We demonstrate the possibility of performing such deep learning-based analysis on 2D X-ray radiography for a faster yet robust method via the X-Robustifier pipeline proposed in this article.Results2D X-ray images of both defective and defect-free seeds were acquired. A deep learning model based on state-of-the-art object detection neural networks is proposed. Specific data augmentation techniques are introduced to compensate for the low ratio of defects and increase the robustness to variation of the physical parameters of the X-ray imaging systems. The seed defects were accurately detected (F1-score >90%), surpassing human performance in computation time and error rates. The robustness of these models against the principal distortions commonly found in actual agro-industrial conditions is demonstrated, in particular, the robustness to physical noise, dimensionality reduction and the presence of seed coating.ConclusionThis work provides a full pipeline to automatically detect common defects in seeds via 2D X-ray imaging. The method is illustrated on sugar beet and faba bean and could be efficiently extended to other species via the proposed generic X-ray data processing approach (X-Robustifier). Beyond a simple proof of feasibility, this constitutes important results toward the effective use in the routine of deep learning-based automatic detection of seed defects.

Remote sensing of high energy particle currents (HEC): towards HEC radiography

Abstract High-energy charged particle current (HEC) originating in or passing through an object creates mirror currents in a system of electrodes surrounding it. The electrodes are not in an electrically conductive contact with the object; they can be placed at a distance from its surface. We employ this effect to probe the object by measuring HEC-radiographic signal for a megavoltage x-ray scanning beam and for a broad static beam. Unlike in standard radiography, where an external bank of detectors measures the emerging radiation from the object, the principle of the proposed HEC radiography relies on remotely sensing the current distribution inside the object. Thus, HEC radiography is capable of probing objects that absorb all or most of the primary incident beam and where transmitted radiation cannot be used for image formation or reconstruction. Potential applications of this technology include non-destructive imaging of radio-opaque objects and in-vivo monitoring of charged particle ranges or dose distribution during cancer radiotherapy.

Growth kinetics, spatialization and quality of potato tubers monitored in situ by MRI - long-term effects of water stress.

Understanding the potato tuber development and effects of drought at key stages of sensitivity on yield is crucial, particularly when considering the increasing incidence of drought due to climate change. So far, few studies addressed the time course of tuber growth in soil, mainly due to difficulties in accessing underground plant organs in a non-destructive manner. This study aims to understand the tuber growth and quality and the complex long-term effects of realistic water stress on potato tuber yield. MRI was used to monitor the growth kinetics and spatialization of individual tubers in situ and the evolution of internal defects throughout the development period. The intermittent drought applied to plants reduced tuber yield by reducing tuber growth and increasing the number of aborted tubers. The reduction in the size of tubers depended on the vertical position of the tubers in the soil, indicating water exchanges between tubers and the mother plant during leaf dehydration events. The final size of tubers was linked with the growth rate at specific developmental periods. For plants experiencing stress, this corresponded to the days following rewatering, suggesting tuber growth plasticity. All internal defects occurred in large tubers and within a short time span immediately following a period of rapid growth of perimedullary tissues, probably due to high nutrient requirements. To conclude, the non-destructive 3D imaging by MRI allowed us to quantify and better understand the kinetics and spatialization of tuber growth and the appearance of internal defects under different soil water conditions.

Benchmarking of X-Ray Fluorescence Microscopy with Ion Beam Implanted Samples Showing Detection Sensitivity of Hundreds of Atoms.

Single impurities in insulators are now often used for quantum sensors and single photon sources, while nanoscale semiconductor doping features are being constructed for electrical contacts in quantum technology devices, implying that new methods for sensitive, non-destructive imaging of single- or few-atom structures are needed. X-ray fluorescence (XRF) can provide nanoscale imaging with chemical specificity, and features comprising as few as 100000 atoms have been detected without any need for specialized or destructive sample preparation. Presently, the ultimate limits of sensitivity of XRF are unknown - here, gallium dopants in silicon are investigated using a high brilliance, synchrotron source collimated to a small spot. It is demonstrated that with a single-pixel integration time of 1 s, the sensitivity is sufficient to identify a single isolated feature of only 3000Ga impurities (a mass of just 350 zg). With increased integration (25 s), 650 impurities can be detected. The results are quantified using a calibration sample consisting of precisely controlled numbers of implanted atoms in nanometer-sized structures. The results show that such features can now be mapped quantitatively when calibration samples are used, and suggest that, in the near future, planned upgrades to XRF facilities might achieve single-atom sensitivity.

A method of three-dimensional muon imaging using cosmic ray ground array

Cosmic ray muon imaging technology is an effective non-destructive imaging technique. It is currently used to survey the internal structure of large-scale objects such as active volcanoes, pyramids, and some buildings. Additionally, it is used to detect high-Z materials in scenarios such as border security, nuclear reactor monitoring, and container inspection. All applications of cosmic ray muons require a detector to reveal and measure the flux or angular variations of muons. However, detectors may have specific characteristics for each application depending on the detection requirements. Unlike single-point track detectors, we propose using ground array detectors to investigate how ground array detection technology can be used for 3D reconstruction of objects. We use ground array detection technology for simulation studies and apply the Iterative Correction Algorithm (ICA) to reconstruct two-dimensional projections and obtain three-dimensional images of density distribution. We have also addressed the convergence problem and analyzed and optimized some parameters that may affect the detection results. The advantages of using ground arrays include their ability to simultaneously measure the transmittance flux data of muons in different directions, achieve multi-angle detection, and have a large field of view, enabling the collection of sufficient data for more accurate detection results. To verify the feasibility of our new method, we conducted a simple experimental validation on a cylindrical cement building located in the Large High Altitude Air Shower Observatory (LHAASO). By comparing the experimental results with the cement building in the array, we found that their shape structure and density information are basically consistent, demonstrating the effectiveness of our method.

Reliability of Three-Dimensional Micro-Computed Tomography Root Canal Analysis Using Drishti Software: A Pilot Study

In recent years, micro-computed tomography (micro-CT) has become a promising tool for non-destructive imaging and the study of root canal morphology in endodontics. However, the reliability of such analyses heavily relies on the accuracy and consistency of the software used for image processing. Objective: This study aims to investigate the reliability of two different micro-CT voxel sizes using open-source three-dimensional (3D) software (Drishti v3.0) used to identify and quantify the Apical Foramen (AF) and Apical Constriction (AC) in mandibular premolars. Methods: Eight mandibular premolars were divided into two groups, with four samples in each group. Samples were scanned using a ZEISS X-Radia 520 Micro-CT system with two different voxel sizes of 23 µm (Group A) and 17 µm (Group B), respectively. The micro-CT data were reconstructed and transferred to Drishti Software, and individual image rendering was performed to visualize the images for data analysis. The position and measurement of AF and AC to the anatomical apex were measured individually and independently by single operators. Results: The reliability of the measurements assessed using Intraclass Correlation Coefficients (ICCs) between two voxel sizes was excellent and on par with previous research. This pilot study suggested that results for voxel sizes of 23 µm and 17 µm were comparable. Both voxel size 3D images demonstrated excellent and high-quality visualization of the root canal configuration, lateral and accessory canals, apical deltas, major AF, and AC. Conclusion: It is feasible and reliable to visualize and analyze 3D micro-CT images using open-source software (Drishti v3.0). Both voxel sizes of 23 µm and 17 µm are recommended for use in root canal study.

Scaling up contrast-enhanced micro-CT imaging: Optimizing contrast and acquisition for large ex-vivo human samples

Microfocus Computed Tomography (Micro-CT) is a novel method for non-destructive 3D imaging of samples, reaching microscale resolutions. While initially prominent in material sciences for small samples, micro-CT now gains significance in biological and medical studies. Here we present our utilization of micro-CT for imaging large ex-vivo human samples for anatomical and forensic research in three recent experiments and discuss the fundamentals of micro-CT imaging.For pelvic anatomical research, whole human pelvises were imaged to explore nerve anatomy around the prostate using various concentrations of buffered lugol (B-lugol). Advanced acquisition protocols were essential due to X-ray attenuation properties of the sample, which required higher energy for sufficient photon transmission.For fetal research, B-lugol stained fetuses of 20–24 gestational weeks underwent full body imaging. However, this led to challenging acquisition parameters and images of insufficient quality. Subsequent destaining yielded less dense, yet contrast-maintaining samples allowing higher quality images. Refined acquisition protocols with reduced energy improved image quality.For forensic research, explanted hyoid-larynx complexes were imaged. Micro-CT imaging showed potential in visualizing micro-fractures. The addition of B-lugol allowed for excellent soft tissue contrast and promising possibilities for forensic evaluation.In conclusion, micro-CT imaging accommodates a diversity of large ex-vivo human samples for anatomical and forensic purposes, though challenges arise with optimal soft tissue staining and acquisition protocols. We describe partial destaining as a new possibility to alleviate scanning issues to improve scan quality and highlight topics for future research. Micro-CT imaging is a promising new avenue for medical research and forensic evaluation.

Laser ultrasonic frequency-domain imaging and phase weighted optimization based on full matrix capture

Far-field laser technology has greatly promoted the progress of nondestructive ultrasonic imaging of bulk structures. However, under thermoelastic excitation, the body waves exhibit a relatively low signal-to-noise ratio, resulting in images with low resolution and contrast. Based on the motivation, this paper developed a frequency-domain phase weighted imaging method to improve the quality of laser ultrasonic defect imaging. Firstly, laser ultrasonic scanning was performed on the sample with artificial transverse hole defects. The cylindrical lens focused line source was used to improve the intensity of the body wave signals, and ensure that there was no damage on the material surface under high laser energies. Then, the frequency-domain phase shift migration (PSM) algorithm was used to perform multimode imaging of defects, achieving frequency-domain synthetic aperture focusing technique (F-SAFT) and total focused method (F-TFM) imaging based on full matrix capture. Furthermore, the phase circular statistical vector (PCSV) was proposed for weighted optimization, which improved the image quality, suppressed the background noise and multimode artifacts. Finally, the imaging quality of several algorithms were discussed. The results indicate that frequency-domain images were superior to time-domain results. After phase weighting, the imaging quality can be further improved, and the detection blind zone was significantly reduced. This work will contribute to the rapid and high-quality defect imaging of laser ultrasonic.

Structural analysis of the female reptile reproductive system by micro-computed tomography and optical coherence tomography†.

Volumetric data provide unprecedented structural insight to the reproductive tract and add vital anatomical context to the relationships between organs. The morphology of the female reproductive tract in non-avian reptiles varies between species, corresponding to a broad range of reproductive modes and providing valuable insight to comparative investigations of reproductive anatomy. However, reproductive studies in reptilian models, such as the brown anole studied here, have historically relied on histological methods to understand the anatomy. While these methods are highly effective for characterizing the cell types present in each organ, histological methods lose the 3D relationships between images and leave the architecture of the organ system poorly understood. We present the first comprehensive volumetric analyses of the female brown anole reproductive tract using two non-invasive, non-destructive imaging modalities: micro-computed tomography (microCT) and optical coherence tomography (OCT). Both are specialized imaging technologies that facilitate high-throughput imaging and preserve three-dimensional information. This study represents the first time that microCT has been used to study all reproductive organs in this species and the very first time that OCT has been applied to this species. We show how the non-destructive volumetric imaging provided by each modality reveals anatomical context including orientation and relationships between reproductive organs of the anole lizard. In addition to broad patterns of morphology, both imaging modalities provide the high resolution necessary to capture details and key anatomical features of each organ. We demonstrate that classic histological features can be appreciated within whole-organ architecture in volumetric imaging using microCT and OCT, providing the complementary information necessary to understand the relationships between tissues and organs in the reproductive system. This side-by-side imaging analysis using microCT and OCT allows us to evaluate the specific advantages and limitations of these two methods for the female reptile reproductive system.

Nondestructive Imaging of Manufacturing Defects in Microarchitected Materials.

Defects in microarchitected materials exhibit a dual nature, capable of both unlocking innovative functionalities and degrading their performance. Specifically, while intentional defects are strategically introduced to customize and enhance mechanical responses, inadvertent defects stemming from manufacturing errors can disrupt the symmetries and intricate interactions within these materials. In this study, we demonstrate a nondestructive optical imaging technique that can precisely locate defects inside microscale metamaterials, as well as provide detailed insights on the specific type of defect.

Synthetic data augmentation for high-resolution X-ray welding defect detection and classification based on a small number of real samples

Deep learning has become the dominant technology in most computer vision tasks. These methods often rely on a large number of labeled sample datasets for training, and in the field of non-destructive testing of welds in industrial manufacturing, weld images with defects are very scarce, and it is still a challenging challenge to construct high-resolution weld defect datasets that meet the requirements. To overcome this limitation, a new data augmentation method for high-resolution X-ray welding defect classification and synthesis based on a small number of real samples is proposed to realize the data augmentation of industrial nondestructive inspection X-ray film defect images. Firstly, to overcome the scarcity of the weld X-ray defect classification dataset, the weld X-ray defect classification dataset (Weld Defect Classification, WDC) is constructed. Secondly, the performance of 16 common deep classification models on WDC datasets is explored. Then, the images of the real local welding defects and the non-defective weld area are fused at random locations, and two data augmentation modes, (Single Image Single Defect, SISD) and (Single Image Multi Defects, SIMD), can generate defect files and annotation files (Visual Object Classes, VOC) at the same time, which can save a lot of time for manual marking. Finally, compared with the traditional data augmentation method, the proposed method can effectively improve the accuracy of defect detection and generalization, the mAP (Mean Average Precision, mAP) @0.5 of YOLOV8X (You Only Look Once, YOLO) and YOLOV5.6.1X is 66.6% and 72.8%, which provides an effective solution for data sample generation in the industrial field.