Search Results (89)

This review traces the four-generation evolution of non-destructive evaluation (NDE 1.0–4.0) and audits where the field genuinely stands today. The central finding is that statistically qualified probability of detection (POD), as defined in MIL-HDBK-1823A and related frameworks, is not interchangeable with machine-learning metrics such as accuracy or F1-score; the two answer different questions and rest on different statistical foundations. Reported AI performance on curated datasets does not, by itself, predict field reliability because domain shift, sensor variability, and class imbalance change the inspection signal once a model leaves the lab. Six recurring barriers limit industrial uptake: scarce open benchmark datasets, domain shift, weak interoperability, explainability constraints, cybersecurity exposure, and the lack of broadly accepted code provisions for AI-derived accept/reject decisions. The oil and gas sector is used as a case study because it combines high inspection volume, severe operating environments, mature risk-based inspection practice, and strong regulatory conservatism. NDE 4.0 is technically credible; its wider acceptance in safety-critical industries will be earned through representative field validation, auditable model governance, standardised data structures, and qualification pathways—not through stronger laboratory accuracy claims. Full article

Cotton, a globally significant crop grown in over 100 countries, sustains a $40 billion market and provides employment for over 350 million people worldwide. However, plastic contamination remains a persistent challenge within the industry, degrading cotton fiber quality and disrupting ginning. Manual inspection and optical machine-vision systems struggle when plastic fragments are concealed by fibers or lack sufficient color contrast. To address these challenges, we developed an ultrasonic phased-array imaging system operating at 40 kHz under field-programmable gate array (FPGA) control. Transmitter elements emit pulsed ultrasound along radial paths, separate reflection receivers record echo amplitudes to form acoustic images, and a set of transmission receivers captures signal attenuation, which is overlaid onto the reflection-based image to highlight potential contaminants. In preliminary laboratory-based tests on both seed cotton and lint samples, the system successfully detected visually obscured plastic fragments as small as $2\mathrm{cm}\times 2\mathrm{cm}$ with an angular resolution limit of $\pm 3°$. Distinct reflection peaks and corresponding attenuation overlays were produced across the field of view, validating the system’s detection capabilities. These results demonstrate the feasibility of using ultrasonic imaging to reveal concealed plastics in cotton processing. Integrating this approach with existing optical methods could enhance contaminant-removal workflows and improve overall fiber quality and processing efficiency. Full article

In this paper, a method for determining the filling level of grooves (1 mm (W) × 0.25 mm (H)) in pressed titanium pipe-fitting joints is presented. The joints are inspected in a water bath using a 20 MHz phased array ultrasound, and the acquired raw B-scans are evaluated by a convolutional neural network that performs per-groove regression. Reference filling levels are obtained destructively from micrographs. Compared to X-ray computed tomography and destructive sectioning, the proposed approach overcomes the low material contrast between pipe and fitting, avoids long scan times, and enables a nondestructive, potentially inline-capable quantitative assessment of sub-millimeter grooves. A manual high-frequency ultrasound evaluation with a single probe and conceivable rule-based time-of-flight pipelines with hand-crafted echo picking and thresholds both show only moderate agreement with CT references and require substantial feature engineering for multiple echoes. In contrast, the PAUT-CNN method exploits the full raw B-scan without explicit feature design and achieves a root mean square error of about 7% of the groove filling levels on a held-out test set, corresponding to an absolute error on the order of a few tens of micrometers in groove height. This demonstrates that high-frequency phased array ultrasound combined with data-driven evaluation can quantitatively assess the filling of sub-millimeter grooves in aerospace-relevant press-fit connections. Full article

The work presents an algorithm for early detection of breast microlesions using a high resolution three-dimensional ultrasound imaging system. The system employs a 1 × 512 ring transducer array and a triaxial displacement platform with an accuracy of 0.1 mm, achieving high-density acquisition of three-dimensional volumetric data through fixed-step scanning. To improve imaging quality, an adaptive beamforming algorithm incorporating optimal sound speed estimation is proposed, effectively compensating for phase distortion caused by sound speed heterogeneity within tissues and improving spatial coherence and imaging resolution. The three-dimensional volumetric data is visualized using volume rendering to achieve high-fidelity three-dimensional ultrasound image reconstruction. The in vitro experimental results demonstrate that the proposed algorithm improves the system’s spatial resolution to 0.5 mm, with a linear measurement accuracy of 2.1%. A preliminary clinical feasibility case study comparing breast image reconstruction with MRI imaging results shows a Dice similarity coefficient of 0.87 for the lesion region, high anatomical structure reconstruction accuracy, and good spatial consistency. These results demonstrate preliminary clinical feasibility for early detection of breast microlesions. Full article

Unmanned aerial vehicles open new possibilities for developing technologies that support more sustainable and efficient agriculture. This paper presents a non-invasive method for repelling rodents from crop fields using ultrasound. The proposed system is implemented as a spherical-cap ultrasound loudspeaker array consisting of eight transducers, mounted on a drone that overflies the field while emitting sound in the 20–70 kHz range. The hardware design includes both the loudspeaker array and a custom printed circuit board hosting power amplifiers and a signal generator tailored to drive multiple ultrasonic transducers. In parallel, a genetic algorithm is used to compute flight paths that maximize coverage and increase the probability of driving rodents away from the protected area. As part of the validation phase, artificial intelligence models for rodent detection using a thermal camera are developed to provide quantitative feedback on system performance. The complete prototype is evaluated through a series of experiments conducted both in controlled laboratory conditions and in the field. Field trials highlight which parts of the concept are already effective and identify open challenges that need to be addressed in future work to move from a research prototype toward a deployable product. Full article

Hyperechoic vertical artifacts are an essential feature of lung ultrasound (LUS) arising from various pathological states. Those that meet the criteria for B-lines have the most significant diagnostic value and should be differentiated from other hyperechoic vertical artifacts of unspecified clinical importance. Although numerous studies have assessed the impacts of transducer type on the appearance of B-lines in human medicine, comparative studies in veterinary medicine are limited and conflicting. This study compares three transducer types for the assessment of hyperechoic vertical artifacts in dogs. We hypothesize that there is high-level reviewer agreement in the assessment of HVA image quality and characteristics, and that the image quality/characteristics differ between the three transducers. Dogs (n = 8) with HVAs and sonographic absence of lung consolidations, pleural effusion, and/or pneumothorax were enrolled. Twenty-four cine-loops (5 s) containing HVAs were retrospectively and independently reviewed by two reviewers, who were blinded to the case details but not transducer type. The reviewers assessed the cine-loops for the following: whether HVAs meet the B-line criteria, ease of counting HVAs, and overall image quality. Paired cine-loops from the same patient using different transducers were then presented for HVA quality comparison. Inter-rater concordance was determined using the Kappa coefficient, Kendall’s tau, and Pearson correlation coefficient, while characteristics were compared using chi-square and Kruskal–Wallis tests (level of significance, α = 0.05). The overall concordance of image quality was good (Pearson’s coefficient = 0.82). The PA transducer scored lower in image quality (p < 0.001), HVA blending (p = 0.014), graininess (p < 0.001), and clarity of edges (p < 0.001) when compared with the microconvex and linear transducers, and the identification of B-line criteria differed between transducers (p = 0.024). Furthermore, the PA scored lowest in the comparison of paired cine-loops regarding the image and HVA quality (p < 0.001). Although more HVAs failed to reach the far field with the linear transducer (10/16, 62.5%) compared with the microconvex (8/16, 50%) and PA (3/16, 18.5%) transducers, the linear transducer scored higher than the microconvex and PA transducers regarding its ability to count B-lines (p < 0.001). This study demonstrates that the type of transducer significantly impacts the characteristics of HVAs, with the PA transducer producing lower-quality images compared with the microconvex and linear transducers. Full article

To address the challenges of automatic detection caused by the variation of surface normal vectors in automotive steering knuckles, an automatic detection method based on ultrasonic phased array technology is herein proposed. First, a point cloud model of the workpiece was constructed using ultrasonic distance measurement, and Gaussian-weighted principal component analysis was used to estimate the normal vectors of the point cloud. By utilizing the normal vectors, water layer thickness during detection, and the incident angle of the sound beam, the probe pose information corresponding to the detection point was precisely calculated, ensuring the stability of the sound beam incident angle during the detection process. At the same time, in the trajectory planning process, piecewise cubic Hermite interpolation was used to optimize the detection trajectory, ensuring continuity during probe movement. Finally, an automatic detection system was set up to test a steering knuckle specimen with surface circumferential cracks. The results show that the point cloud data of the steering knuckle specimen, obtained using phased array ultrasound, had a relative measurement error controlled within 1.4%, and the error between the calculated probe angle and the theoretical angle did not exceed 0.5°. The probe trajectory derived from these data effectively improved the B-scan image quality during the automatic detection of the steering knuckle and increased the defect signal amplitude by 5.6 dB, demonstrating the effectiveness of this method in the automatic detection of automotive steering knuckles. Full article

Hand gesture recognition (HGR) is a convenient and natural form of human–computer interaction. It is suitable for various applications. Much research has already focused on wearable device-based HGR. By contrast, this paper gives an overview focused on device-free HGR. That means we evaluate HGR systems that do not require the user to wear something like a data glove or hold a device. HGR systems are explored regarding technology, hardware, and algorithms. The interconnectedness of timing and power requirements with hardware, pre-processing algorithm, classification, and technology and how they permit more or less granularity, accuracy, and number of gestures is clearly demonstrated. Sensor modalities evaluated are WIFI, vision, radar, mobile networks, and ultrasound. The pre-processing technologies stereo vision, multiple-input multiple-output (MIMO), spectrogram, phased array, range-doppler-map, range-angle-map, doppler-angle-map, and multilateration are explored. Classification approaches with and without ML are studied. Among those with ML, assessed algorithms range from simple tree structures to transformers. All applications are evaluated taking into account their level of integration. This encompasses determining whether the application presented is suitable for edge integration, their real-time capability, whether continuous learning is implemented, which robustness was achieved, whether ML is applied, and the accuracy level. Our survey aims to provide a thorough understanding of the current state of the art in device-free HGR on edge devices and in general. Finally, on the basis of present-day challenges and opportunities in this field, we outline which further research we suggest for HGR improvement. Our goal is to promote the development of efficient and accurate gesture recognition systems. Full article

In exploring adjuvant therapies for head and neck cancer, hyperthermia (40–45 °C) has shown efficacy in enhancing chemotherapy and radiation, as well as the delivery of liposomal drugs. Current hyperthermia treatments, however, struggle to reach large deep tumors uniformly and non-invasively. This study investigates the feasibility of delivering targeted uniform hyperthermia deep into the tissue using a non-invasive ultrasound spherical random phased array transducer. Simulations in 3D patient-specific models for thyroid and oropharyngeal cancers assessed the transducer’s proficiency. The transducer consisting of 256 elements randomly positioned on a spherical shell, operated at a frequency of 1 MHz with various phasing schemes and power modulations to analyze 40, 41, and 43 °C isothermal volumes and the penetration depth of the heating volume, along with temperature uniformity within the target area using T10, T50, and T90 temperatures, across different tumor models. Intensity distributions and volumetric temperature contours were calculated to define moderate hyperthermia boundaries. The results indicated the array’s ability to produce controlled heating volumes from 1 to 48 cm³ at 40 °C, 0.35 to 27 cm³ at 41 °C, and 0.1 to 8 cm³ at 43 °C. The heating depths ranged from 7 to 39 mm minimum and 52 to 59 mm maximum, measured from the skin’s inner surface. The transducer, with optimal phasing and water-cooled bolus, confined the heating to the targeted regions effectively. Multifocal sonications also improved the heating homogeneity, reducing the length-to-diameter ratio by 38% when using eight foci versus a single one. This approach shows potential for treating a range of tumors, notably deep-seated and challenging oropharyngeal cancers. Full article

This study investigates the application of Phased Array Corrosion Mapping (PACM) as a non-destructive testing (NDT) method for detecting and monitoring corrosion growth on hot stainless steel (SS) surfaces, specifically focusing on SS 304 and SS 316. Conducted across a temperature range of 30 °C to 250 °C, the research evaluates the effectiveness of PACM in high-temperature environments typical of the petrochemical industry. Experiments were conducted using specimens with machined slots and flat-bottom holes (FBHs) to simulate corrosion defects. The results demonstrate that PACM effectively detects and maps corrosion indicators, with color-coded C-scan data facilitating easy interpretation. Temperature variations significantly influenced ultrasound signal characteristics, leading to observable changes in FBH indications, particularly at elevated temperatures. Increased ultrasound attenuation necessitated adjustments in decibel settings to maintain accuracy. SS 304 and SS 316 exhibited distinct responses to temperature changes, with SS 316 showing higher dB values and unique signal behaviors, including increased scattering and noise echoes at elevated temperatures. Detected depths for slots and FBHs correlated closely with designed depths, with deviations generally less than 0.5 mm; however, some instances showed deviations exceeding 2 mm, underscoring the need for careful interpretation. At temperatures above 230 °C, the disbanding of probe elements led to weak or absent signals, complicating data interpretation and requiring adjustments in testing protocols. This study highlights the feasibility and effectiveness of PACM for corrosion detection on hot SS surfaces, providing critical insights into material behavior under thermal conditions. Future research should include physical examination of samples using Scanning Electron Microscopy (SEM) to validate and enhance the reliability of the findings. The integration of non-contact NDT methods and optimization of calibration techniques are essential for improving PACM performance at elevated temperatures. Full article

Acoustic holography technology is widely used in the field of ultrasound due to its capability to achieve complex acoustic fields. The traditional acoustic holography method based on single-phase holograms is limited due to its inability to complete acoustic field control with high dynamics and accuracy. Here, we propose a method for constructing an acoustic holographic model, introducing an ultrasonic array to provide dynamic amplitude control degrees of freedom, and combining the dynamically controllable ultrasonic array and high-precision acoustic hologram to achieve the highest acoustic field accuracy and dynamic range. This simulation method has been proven to be applicable to both simple linear patterns and complex surface patterns. Moreover, it is possible to reconstruct the degree of freedom of the target plane amplitude effectively and achieve a breakthrough in high information content. This high-efficiency acoustic field control capability has potential applications in ultrasound imaging, acoustic tweezers, and neuromodulation. Full article

Background/Objectives: Focused ultrasound (FUS) and microbubble (MB) exposure is a promising technique for targeted drug delivery to the brain; however, refinement of protocols suitable for large-volume treatments in a clinical setting remains underexplored. Methods: Here, the impacts of various sonication parameters on blood–brain barrier (BBB) permeability enhancement and tissue damage were explored in rabbits using a clinical-prototype hemispherical phased array developed in-house, with real-time 3D MB cavitation imaging for exposure calibration. Initial experiments revealed that continuous manual agitation of MBs during infusion resulted in greater gadolinium (Gd) extravasation compared to gravity drip infusion. Subsequent experiments used low-dose MB infusion with continuous agitation and a low burst repetition frequency (0.2 Hz) to mimic conditions amenable to long-duration clinical treatments. Results: Key sonication parameters—target level (proportional to peak negative pressure), number of bursts, and burst length—significantly affected BBB permeability enhancement, with all parameters displaying a positive relationship with relative Gd contrast enhancement (p < 0.01). Even at high levels of BBB permeability enhancement, tissue damage was minimal, with low occurrences of hypointensities on T2*-weighted MRI. When accounting for relative Gd contrast enhancement, burst length had a significant impact on red blood cell extravasation detected in histological sections, with 1 ms bursts producing significantly greater levels compared to 10 ms bursts (p = 0.03), potentially due to the higher pressure levels required to generate equal levels of BBB permeability enhancement. Additionally, albumin and IgG extravasation correlated strongly with relative Gd contrast enhancement across sonication parameters, suggesting that protein extravasation can be predicted from non-invasive imaging. Conclusions: These findings contribute to the development of safer and more effective clinical protocols for FUS + MB exposure, potentially improving the efficacy of the approach. Full article

In this study, a phase-generated carrier (PGC) demodulation algorithm combined with the extended Kalman filter (EKF) algorithm based on an electro-optic modulator (EOM) is proposed, which can achieve nonlinear distortion (such as modulation depth drift and carrier phase delay) suppression for high-frequency phase carrier modulation. The improved algorithm is implemented on a field-programmable gate array (FPGA) hardware platform. The experimental results by the PGC-EKF method show that total harmonic distortion (THD) decreases from −32.61 to −54.51 dB, and SINAD increases from 32.59 to 47.86 dB, compared to the traditional PGC-Arctan method. This indicates that the PGC-EKF demodulation algorithm proposed in this paper can be widely used in many important fields such as hydrophone, transformer, and ultrasound signal detection. Full article

Plane wave imaging persists as a focal point of research due to its high frame rate and low complexity. However, in spite of these advantages, its performance can be compromised by several factors such as noise, speckle, and artifacts that affect the image quality and resolution. In this paper, we propose an attention-based complex convolutional residual U-Net to reconstruct improved in-phase/quadrature complex data from a single insonification acquisition that matches diverging wave imaging. Our approach introduces an attention mechanism to the complex domain in conjunction with complex convolution to incorporate phase information and improve the image quality matching images obtained using coherent compounding imaging. To validate the effectiveness of this method, we trained our network on a simulated phased array dataset and evaluated it using in vitro and in vivo data. The experimental results show that our approach improved the ultrasound image quality by focusing the network’s attention on critical aspects of the complex data to identify and separate different regions of interest from background noise. Full article

Sarcopenia, the age-related loss of skeletal muscle mass, is a core component of frailty that is associated with functional decline and adverse health events in older adults. Unfortunately, the available tools to diagnose sarcopenia are often inaccessible or not user-friendly for clinicians. Point-of-care ultrasound (US) is a promising tool that has been used to image the quadriceps muscle and measure its thickness (QMT) as a diagnostic criterion for sarcopenia. This measurement can be challenging for clinicians, especially when performed at the bedside using handheld systems or phased-array probes not designed for this use case. In this paper, we sought to automate this measurement using deep learning methods to improve its accuracy, reliability, and speed in the hands of untrained clinicians. In the proposed framework, which aids in better training, particularly when limited data are available, convolutional and transformer-based deep learning models with generic or data-driven pre-trained weights were compared. We evaluated regression (QMT as a continuous output in cm) and classification (QMT as an ordinal output in 0.5 cm bins) approaches, and in the latter, activation maps were generated to interpret the anatomical landmarks driving the model predictions. Finally, we evaluated a segmentation approach to derive QMT. The results showed that both transformer-based models and convolutional neural networks benefit from the proposed framework in estimating QMT. Additionally, the activation maps highlighted the interface between the femur bone and the quadriceps muscle as a key anatomical landmark for accurate predictions. The proposed framework is a pivotal step to enable the application of US-based measurement of QMT in large-scale clinical studies seeking to validate its diagnostic performance for sarcopenia, alone or with ancillary criteria assessing muscle quality or strength. We believe that implementing the proposed framework will empower clinicians to conveniently diagnose sarcopenia in clinical settings and accordingly personalize the care of older patients, leading to improved patient outcomes and a more efficient allocation of healthcare resources. Full article