Simulation-based design of a gamma-source compton backscatter imaging system for intelligent pipeline inspection gauge (PIG) applications in sour gas pipelines

Backscatter imaging is a reliable method of single-sided X-ray imaging where access may pose limitations on traditional transmission X-ray image capture. Because of physical challenges in its implementation, it has traditionally been regarded as unable to do material discrimination, a capability reserved for dual energy and transmission imaging by industry and academia. This research expanded on a novel method developed at the University of Florida, where a material calibration tool was used to calibrate a Viken Detection Nighthawk BTX in backscatter mode for postprocessing material discrimination. Namely, this novel approach identifies materials potentially found in explosive devices, i.e. lead, copper, steel, and plastic, as well as combinations of these materials through a fusion of filters. This research focused on expanding the previous work to determine if the novel approach worked at distances beyond the original use-based tests, as well as if source beam filtering and hardening would improve material resolution.

Combining multi-wavelength laser backscattering imaging and machine learning techniques for non-destructive assessment of kiwifruit ripeness

Customs services perform X-ray security screening to prevent illicit articles from entering countries. Primary contraband products illegally distributed include unspecified types of narcotics, explosives, and seeds. The challenge in detecting contraband items using only a conventional transmission X-ray inspection system stems from their tendency to be concealed within thin layers in luggage, in addition to their low atomic number and density. Meanwhile, for such materials, backscatter X-ray imaging, based on Compton scattering, has been shown to provide superior image contrast when compared with transmission imaging. Due to these advantages, backscatter imaging has been extensively adopted for the detection of contraband in baggage, vehicles, and port containers. In the present study, the reliability of the Geant4 (GEometry ANd Tracking4) simulation model for a recently developed backscatter X-ray security scanner was experimentally validated. To this end, a comparative analysis was performed for X-ray source spectra and backscatter image profiles obtained through both experiments and simulation models. The tube voltages were 50, 80, and 100 kV, with a tube current of 1 mA. To obtain the backscatter X-ray images, the object was scanned by a collimated pencil-beam while moving on a conveyor belt. In the simulation, the X-ray source term was obtained by means of monoenergetic electron-beam bombardment onto the target. Then, the scanning pencil-beam and moving object were simulated using 4-D simulations. The percent differences between the simulated and measured source spectra and the image profiles were all found to be within 5%. Consequently, it was determined that the simulation model for the backscatter X-ray security scanner can be relied upon.

Rapid and Economical Detection of Adulterated Heterotrigona itama Honey Using InceptionV3 and Backscattering Imaging

Nondestructive ultrasonic characterization of a triple-weld-bead wire arc additively manufactured ER70S-6 S-curved wall

Concept development of an active fast neutron backscatter imaging technique for planetary subsurface studies

Monitoring of Bell Pepper Moisture Content via Laser Backscattering Imaging and Multivariate Data Analysis

HighlightsWhat are the main findings?A decomposition theory for endmember backscattering contributions is developed.A novel estimation scheme for endmember backscattering coefficients is proposed.What are the implications of the main findings?Offer a physical basis for unmixing radar signals within mixed pixels.Help to construct accurate backscattering-based parameter estimation models.The complexity of land types and the limited spatial resolution of Synthetic Aperture Radar (SAR) imagery have led to widespread mixed-pixel contamination in radar backscatter images. The radar backscatter echo signals from a mixed pixel are often a combination of backscattering contributions from multiple endmembers. The signal mixture of endmembers within mixed pixels hinders the establishment of accurate relationships between pure endmembers’ parameters and the corresponding backscatter coefficient, thereby significantly reducing the accuracy of surface parameter inversion. However, few studies have focused on decomposing and estimating the pure backscatter signals within mixed pixels. This paper proposes a novel approach based on hyperspectral unmixing techniques and the microwave backscatter contribution decomposition (MBCD) model to estimate the pure backscatter coefficients of all Endmembers within mixed pixels. Experimental results demonstrate that the model performance varied significantly with endmember abundance. Specifically, high accuracy was achieved in estimating soil backscattering coefficients when vegetation coverage was below 25% (, with 98% of pixels showing relative errors within 0–20%); however, this accuracy declined as vegetation coverage increased. For grass endmembers, the model maintained high estimation precision across the entire grassland area (vegetation coverage 0.2–0.8), yielding an of 0.80 with 83% of pixels falling within the 0–20% relative error range. In addition, the model performance is influenced by the number of endmembers.

Design and implementation of laser-light backscattering imaging system as a non-destructive technique for citrus taste evaluation

An accurate distinction between brain tumors and tumorless brain tissue is crucial for effective surgical resection. Polarization-sensitive optical imaging exploits birefringence differences, offering contrast between the optically anisotropic white matter of the tumorless brain and the optically isotropic brain tumor tissue. However, crossing brain fiber bundles within tumorless brain tissue may also erase such optical anisotropy. We use a polarized Monte Carlo algorithm to model backscattered wide-field Mueller matrix images of the optical phantoms of the brain's white matter. We compare the impact of fiber bundle crossing and the presence of an optically isotropic subsurface tumor across varying depths to mimic brain tissue removal during neurosurgery. The simulation results demonstrate that the depolarization dependence on depth may serve as a decisive parameter to distinguish the tumor and fiber bundles crossing zones, as the values of linear retardance drop in both zones, whereas the depolarization values become smaller in the tumor zone.

Monitoring defects such as voids and foreign inclusions in building walls is critical for ensuring structural safety. Conventional methods like infrared thermography suffer from environmental interference, impact-echo techniques lack precise localization, and ultrasonic testing offers limited visualization. To address these limitations, we design a wall defect detection system based on Compton backscattering imaging and conduct simulation studies using Geant4. The detection system employs a radiation source emitting a fan-beam collimated by a front collimator to scan the wall model. Backscattered photons generated via Compton scattering are then collimated by a rear collimator and detected by a 256 × 256 pixel NaI(Tl) scintillator array detector. The system reconstructs cross-sectional density distribution images from scattered photon counts. After acquiring multiple layers of scans, image correction, noise reduction (Gaussian and median filtering), and threshold segmentation are applied, followed by three-dimensional (3D) reconstruction using volume rendering to extract structural details of internal defects. Geant4 simulations demonstrate that the system accurately identifies defect materials in concrete walls, with a positional error of less than 2%, a 3D reconstruction resolution of 1.0 mm, and both volume and surface area reconstruction errors below 4%.

Identifying concealed explosives in X-ray backscatter (XRBS) imagery remains a critical challenge, primarily due to low image contrasts, cluttered backgrounds, small object sizes, and limited structural details. To address these limitations, we propose YOLOv11-XRBS, an enhanced detection framework tailored to the characteristics of XRBS images. A dedicated dataset (SBCXray) comprising over 10,000 annotated images of simulated explosive scenarios under varied concealment conditions was constructed to support training and evaluation. The proposed framework introduces three targeted improvements: (1) adaptive architectural refinement to enhance multi-scale feature representation and suppress background interference, (2) a Size-Aware Focal Loss (SaFL) strategy to improve the detection of small and weak-feature objects, and (3) a recomposed loss function with scale-adaptive weighting to achieve more accurate bounding box localization. The experiments demonstrated that YOLOv11-XRBS achieves better performance compared to both existing YOLO variants and classical detection models such as Faster R-CNN, SSD512, RetinaNet, DETR, and VGGNet, achieving a mean average precision (mAP) of 94.8%. These results confirm the robustness and practicality of the proposed framework, highlighting its potential deployment in XRBS-based security inspection systems.

• Early bruise detection in tomatoes using laser-light backscattering imaging (LLBI). • GLCM was used to extract texture features from tomato backscattering images. • Support vector machine algorithm used to build a model for bruise classification. • LLBI technique achieved 96.111 % accuracy in identifying early bruises in tomatoes. • This innovative LLBI can optimize postharvest quality monitoring of horticulture. Accurately detecting bruise in tomatoes is a significant challenge in postharvest processing due to large-scale production and the demand for high-quality standards. Reduced nutritional value and product appearance due to bruise can affect market value and selling price. Therefore, this study aimed to develop a sensing technology based on laser-light backscattering imaging (LLBI) to identify the bruise of tomatoes. A total of 300 samples were collected and labeled into two main classes namely sound and bruised tomatoes. Each fruit was scanned with various laser wavelengths (450, 532, and 648 nm) at an incident angle of 20° to monitor the structural characteristics. In backscattering image, the gray-level co-occurrence matrix was implemented to extract six texture features including contrast, dissimilarity, homogeneity, energy, angular second moment, and correlation. Additionally, a support vector machine with various kernel functions namely linear, radial basis function (RBF), and polynomial was used to detect bruise. The results showed that based on numerical analysis, the LLBI technique was capable of identifying bruise in tomatoes with an accuracy of 96.111 %. The proposed technique implemented the best combination of laser wavelength and kernel function of 648 nm and RBF, respectively. Therefore, this innovative LLBI technique has the potential to optimize quality monitoring of horticultural products during postharvest handling, reduce fruit rejection rates, and prevent financial losses in the agro-industrial sector.

Seasonal monsoon rains and upstream water inflows make the Lower Ganges Basin one of the most flood-prone regions in India. Bagmati, Gandak, Burhi Gandak, Adhvara group and Kosi, coupled with heavy rainfall, contribute significantly to the flooding, which havoc on people’s settlements and the naturally built environment. This comprehensive research was conducted to map the flood inundated area of the 2021 flood, using the time-series sentinel-1 SAR data from May to November in central part of Bihar. To accurately delineate flooded areas, an optimal threshold-based classification was applied to VH-polarized backscatter images, with threshold values ranging from − 19.5 dB to -22.3 dB. Fourteen Sentinel-1 images were processed in SNAP, followed by a detailed flood impact assessment on Land Use and Land Cover (LULC) using ArcMap. In this process, we carefully examine various LULC categories—such as cultivated land, Built-up, open scrub, trees, and bare ground—to quantify the extent of the flood’s impact. The key results show that the months of July and August marked the peak of the disaster, with inundated areas measuring an astounding 4,453 km² and 4,400 km², respectively. Overall, the floodwaters overtook 4.22%, 10.42%, 10.30%, 9.22%, 5.24%, and 3.22% of the land in June, July, August, September, October, and November 2021, respectively. Most alarmingly, our analysis underscored the severe impact on land use and land cover (LULC), particularly affecting agricultural lands, with approximately 4,159 km² of cultivated areas submerged during the peak flooding months. The data and methodologies we employed can be adapted for use in flood mapping across the globe, empowering communities to better prepare for and respond to future disasters.

Dual-energy X-ray backscatter imaging based on attenuation coefficient decomposition

Mueller matrix polarimetry (MMP) has been proven to be a powerful tool for characterizing the microstructural features of biological samples in biomedical research and clinical diagnostics. However, the traditional Mueller matrix (MM) imaging technique based on single exposure has a limited dynamic range, leading to poor polarization image quality for biological samples with significant contrast variations. In this study, we propose a novel method to generate high dynamic range (HDR) MM images based on a multi-exposure fusion algorithm. By employing an optimal exposure selection strategy for transmission imaging and a multi-exposure weighted averaging strategy for backscattering imaging, the method expands the dynamic range while accurately preserving the polarization information of the samples. Experiments of sliced and bulk tissues demonstrate that the proposed method significantly suppresses the scattering noise and improves the quality of extracted polarization parameter images, especially in accurate distinction of different pathological areas. These results highlight the potential of HDR MM imaging technology in extracting polarization information from complex biological samples with high resolution and contrast.

Compressive X-ray Compton backscattering imaging (CXBI) is a recently proposed technique where coded illumination patterns are projected onto a target for security inspection scans while the needed radiation dose decreases. CXBI resembles the well-known concept of single-pixel imaging, as the number of backscattered photons per illumination pattern is captured in large scintillation plates with null spatial resolution. Although CXBI represented a paradigm change in Compton scanning, no further studies to explore optimal coding structures have been yet released. In this sense, this paper proposes an in-depth analysis of the CXBI sensing matrix, to formulate model-based and data-driven solutions to find adequate coding patterns that maximize the quality of the recovered scenes. The proposed cost functions, in both cases, are guided through defined features such as transmittance, mutual coherence, a binary restriction, and dispersion of ON pixels. The non-data-driven approach is conceived as a gradient-descent problem with reconstructions done through the alternating direction method of multipliers (ADMM) with block-matching and 3D filtering (BM3D) as a prior denoiser. Data-driven, on the other hand, uses a sampling stage combined with residual U-blocks for training while residual U-blocks for reconstruction. Training data consists of human silhouette images, hand-written letters, and self-generated scenes, which are contaminated with Geant-4 noise similar to the one that affects CXBI in a real scenario. The encountered optimal patterns were tested using the Geant-4 Application for Tomographic Emissions (GATE) under realistic conditions and compared against randomly generated patterns. Our results demonstrate that non-data- and data-driven designed codes outperform random codes, with the data-driven solution yielding superior quality metrics.

Simulation Model for Compressive X-ray Compton Backscattering Imaging Based on Coded Illumination

High-sensitivity fiber scintillator detectors are the key to achieving high signal-to-noise ratio and high contrast imaging in X-ray Compton backscattering technology. We established a simulation model of wavelength-shifting fiber (WSF) scintillator detectors based on Geant4. The influences of ray source energy, detection area, number of WSFs, and coupling mechanism on detection efficiency were simulated. By using the epoxy resin coupling method, the transmission efficiency between the WSF and scintillator was increased from 4.56% to 19.79%. Based on the simulation data, we developed an X-ray WSFs scintillator detector, built an X-ray backscattering imaging experimental system, obtained high-contrast backscattering images, and verified the performance of the detector.