Real-time System Research Articles

Machine learning-driven IoT device for women’s safety: a real-time sexual harassment prevention system

Machine learning-driven IoT device for women’s safety: a real-time sexual harassment prevention system

Impact of Amphiregulin on Oocyte Maturation and Embryo Quality: Insights from Clinical and Molecular Perspectives

AbstractIdentifying non-invasive biomarkers which can predict the outcome of intracytoplasmic sperm injection (ICSI) is crucial, particularly in Germany where the challenges are intensified by the Embryo Protection Act. Recent research has highlighted biomarkers within the epidermal growth factor (EGF) family as central to follicular processes, although their predictive utility remains a subject of debate in the literature. Therefore, the primary objective of this study was to investigate the significance of amphiregulin concentrations in follicular fluid and gene expression in mural granulosa cells on oocyte maturation, fertilization, and embryo quality.A total of 33 women were recruited at the University Clinic of Saarland Fertility Center (Homburg, Germany). Follicular fluid aspiration consisted of single/individual aspiration of follicles, enabling a 1 : 1 correlation with retrieved oocytes. Follicular fluid and mural granulosa cell samples from 108 oocytes were analyzed. Amphiregulin levels were determined with enzyme-linked immunosorbent assay, while gene expression was analyzed with the StepOnePlus Real-Time PCR System using TaqMan Fast Advanced Master Mix assays.Results showed that amphiregulin concentrations affect oocyte maturation, fertilization, and embryo quality, while luteinizing hormone concentrations influence oocyte maturation, with significant differences identified between fertilized/unfertilized and good/poor embryo groups. Amphiregulin expression significantly impacts oocyte maturation, with downregulation observed in immature oocytes, while luteinizing hormone/chorionic gonadotropin receptor expression showed no significant differences between groups and did not influence maturation, fertilization, or embryo quality.These findings are very important for advancing infertility treatment, especially in Germany. The results for amphiregulin may provide prognostic insights which could be useful when selecting viable oocytes and embryos. This research underscores the importance of non-invasive biomarkers for optimizing ICSI outcomes and potentially enhancing the success rates of assisted reproductive technology.

Integration of Payload Sensors to Enhance UAV-Based Spraying

This work focuses on the use of load sensors to help with spraying tasks using unmanned aerial vehicles (UAVs). The study details the construction of a prototype for load measurement to validate the proof of concept. To simulate the application of agricultural pesticides, the UAV follows a predefined route and an image processing system detects the presence of diseased plants. After detection, the UAV pauses its route momentarily and activates the spraying device. The payload sensor monitors the fertilizer application process, which determines whether the amount of pesticide has been fully applied. If the storage tank is empty or the remaining quantity is insufficient for another operation, the system will command the UAV to return to the base station for refueling. Experimental validations were carried out in an indoor controlled environment to verify the proposal and the functionality of the in-flight payload monitoring system. Additionally, the UAV’s flight controller demonstrated robust performance, maintaining stability despite the challenges posed by liquid-load oscillations and varying payloads during the spraying process. In summary, our main contribution is a real-time payload monitoring system that monitors weight during flight to avoid over- or under-spraying. In addition, this system supports automatic refueling, detecting low levels of pesticides and directing the UAV to return to base when necessary.

EHealth in pediatric respiratory allergy.

This review explores the relevance of eHealth technologies to address unmet needs in pediatric respiratory allergies, particularly allergic rhinitis (AR) and asthma. Given the increasing burden of these conditions, there is a pressing need for effective solutions to enhance disease surveillance, diagnosis, and management. Recent literature highlights the potential of eHealth tools to transform pediatric respiratory allergy care. The use of digital data for infodemiology, application of machine learning models to improve diagnostic sensitivity, smartphone apps with digital patient reported outcome measure (PROMs) and embedded sensors to monitor disease, healthcare professional dashboards with real-time data monitoring and clinical decision support systems (CDSS) are advances emerging to optimize pediatric respiratory allergy care. Integrating eHealth technologies into the pediatric respiratory allergy care pathway is a potential solution for current healthcare challenges to better meet the needs of children with AR and asthma. However, while the potential of eHealth is evident, its widespread implementation in real-world practice requires continued research, collaboration, and efforts to overcome existing barriers.

Abstract A079: SB-216 inhibits oncogenic beta-tubulin subtypes in PDAC

Abstract Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest malignancies, with only 12% of patients living longer than five years after diagnosis. Recent studies have shown that inhibiting microtubule dynamics may be a promising therapeutic avenue for treating patients with this cancer. Microtubules are critical in cancer cell growth and division, as well as intracellular cytoskeletal support and internal trafficking. In PDAC, βIII- and βIVb-tubulin are highly upregulated in contrast to normal pancreas cells. Decreased expression of βIII- and βIVb-tubulin, through silencing or inhibition, correlates with reduced PDAC cell growth, tumorigenic potential, metastases in xenograft models, and chemoresistance. We hypothesized that a novel colchicine binding site beta-tubulin inhibitor, SB-216, will decrease cell growth while also decreasing the expression of oncogenic βIII- and βIVb-tubulin. To examine the anti-proliferative effects, PDCL110, a PDAC patient-derived cell line, cells were seeded (6,000 cells/well), and after 24 hours, treated with 0.1% DMSO (control), 2 nM, 5 nM, 10 nM, or 15 nM SB-216. Plates were analyzed on the IncuCyte Live-Cell Analysis System for six days, where phase contrast images of confluency were captured every four hours. To characterize the mRNA expression of βIII- and βIVb-tubulin, Panc-1 and PDCL110 cells were treated with 0.1% DMSO and 5 nM SB-216 for 48 hours. Next, total RNA was extracted using a TRIzol reagent. cDNAs were prepared using the SYBR Green RNA Reverse Transcription kit, and mRNA expression of beta-tubulin isotypes was quantified using the QuantStudio Real-Time PCR system. The expression of tubulin isotypes was normalized with the internal control ACTIN. Cell growth curves illustrated a significant (p<0.006) decrease in proliferation after 48 hours of treatment at all tested concentrations of SB-216. After 72 hours, cell growth inhibition became more significant (p<0.0001) in a dose-dependent manner. SB-216 treatment of a commercially available PDAC cell line (Panc-1) led to decreased mRNA expression of TUBB3 (class βIII, p<0.05) and TUBB4B (class βIVb, p<0.05) compared to DMSO control. Similarly, TUBB4B expression was significantly (p<0.05) decreased in the PDCL110 cells upon SB-216 treatment. These results enhance our confidence in utilizing SB-216 as a therapeutic agent against PDAC. Future studies are directed at elucidating the effects of SB-216 on metastases, chemoresistance, and mitochondrial function. Through an improved understanding of the mechanisms behind microtubule inhibition, we can meet the urgent clinical need of finding targeted treatments for this complex cancer. Citation Format: Lauren C Gattie, Chun Cai, Rui Wang, Wei Li, Evan Glazer. SB-216 inhibits oncogenic beta-tubulin subtypes in PDAC [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr A079.

Real-time two-dimensional visualization reveals the transport mechanisms of biochar colloids in the presence of DOM in porous media

Biochar colloids (BCs) have attracted much attention globally, and their fate and transport in the subsurface are significantly influenced by soil-dissolved organic matter (DOM) in soil. This study utilized a real-time and non-invasive visualization system to reveal the transport and retention behavior of BCs in the presence of DOM in two-dimensional porous media. Results indicated that the presence of DOM enhanced the transport of BCs, due to its increased negative charge density and increased repulsion between BCs. The change in the particle size of the porous medium was also shown to affect the BCs transport in the porous media. Additionally, the negative charge of BCs shielded by high IS, the mobility of BCs decreased by 30.37 % from 1 mM to 50 mM. When the pH was increased from 5 to 9, the oxygen-containing functional groups of BCs and DOM were dissociated, and the mobility of BCs increased by 32.41 %. Through a simplified Double-Monod model, we fitted the breakthrough curves for BCs transport in porous media (R2 > 0.94). Moreover, the mechanism of different conditions on colloid clogging behavior was further elucidated through the DLVO theory. These findings extend the understanding of the environmental behavior of BCs in the presence of DOM derived from soil, enabling us to assess better and predict their environmental risks.

Study on penetration depth in laser welding: A process information database-based control strategy and OCT measuring verification

Penetration depth acts as a crucial indicator reflecting laser welding quality, thus the control of its stability and the perception of its fluctuation state are increasingly garnering attention. This paper proposes a process information database-based control strategy for penetration depth, and the control validity is verified through penetration depth detection utilizing optical coherence tomography (OCT). The process information database stores diverse expected penetration depth knowledge formed by a substantial quantity of varying welding speeds with fixed other process parameters under undisturbed welding conditions. In the database, the stable average values inside the standard penetration depth information and the corresponding heat input (HI) values are connected and mapped via an artificial neural network (ANN). In response to abnormal variations in the penetration depth curve caused by interferences during welding, according to the HI gap predicted by the trained ANN from the penetration depth gap arising from the curve deviation, the control unit can calculate the new welding speed required to feed the penetration depth curve back to within the steady fluctuation range. Based on OCT, the keyhole depth signal is acquired, and a deep belief network is built to predict the penetration depth curve via the correlation between the reconstructed keyhole depth obtained by ensemble empirical mode decomposition and the penetration depth. This detection method demonstrates that the penetration depth curve can be controlled accurately. Finally, a closed-loop real-time feedback control system for penetration depth is established.

Application of a Real-Time Field-Programmable Gate Array-Based Image-Processing System for Crop Monitoring in Precision Agriculture

Precision agriculture (PA) technologies combined with remote sensors, GPS, and GIS are transforming the agricultural industry while promoting sustainable farming practices with the ability to optimize resource utilization and minimize environmental impact. However, their implementation faces challenges such as high computational costs, complexity, low image resolution, and limited GPS accuracy. These issues hinder timely delivery of prescription maps and impede farmers’ ability to make effective, on-the-spot decisions regarding farm management, especially in stress-sensitive crops. Therefore, this study proposes field programmable gate array (FPGA)-based hardware solutions and real-time kinematic GPS (RTK-GPS) to develop a real-time crop-monitoring system that can address the limitations of current PA technologies. Our proposed system uses high-accuracy RTK and real-time FPGA-based image-processing (RFIP) devices for data collection, geotagging real-time field data via Python and a camera. The acquired images are processed to extract metadata then visualized as a heat map on Google Maps, indicating green area intensity based on romaine lettuce leafage. The RFIP system showed a strong correlation (R2 = 0.9566) with a reference system and performed well in field tests, providing a Lin’s concordance correlation coefficient (CCC) of 0.8292. This study demonstrates the potential of the developed system to address current PA limitations by providing real-time, accurate data for immediate decision making. In the future, this proposed system will be integrated with autonomous farm equipment to further enhance sustainable farming practices, including real-time crop health monitoring, yield assessment, and crop disease detection.

Zinc cobaltite embedded in binary metal-organic frameworks using a direct binder-free electrode fabrication approach for efficient energy storage devices and monosodium glutamate detection

There is an imperative need to develop high performance electrode for advance energy storage devices. This work represents the successful synthesis of zinc cobaltite (ZnCo2O4), nickel cobalt metal organic framework, and their composite (ZnCo2O4@NiCo-MOF). The materials are synthesized by using both hydrothermal and sonication techniques. The ZnCo2O4 offers eco-friendly and diffusion regulated properties, while NiCo-MOF provides large specific surface area and tunable pore structure. The ZnCo2O4@NiCo-MOF provide superior electrochemical performance compared to ZnCo2O4 and NiCo-MOF in a three electrode setup. The ZnCo2O4@NiCo-MOF provides a specific capacity of 984 C/g at 1.5 A/g. The ZnCo2O4@NiCo-MOF is also utilized in two electrode setup with activated carbon. The ZnCo2O4@NiCo-MOF//AC device demonstrates the energy density of 92.4 Wh/kg and a power density of 930 W/kg. Additionally, the real time application of ZnCo2O4@NiCo-MOF electrode is also tested in the detection of monosodium glutamate in food. This study highlights the importance of ZnCo2O4@NiCo-MOF electrode materials for future storage and detection devices.

Research on prediction of yellow flesh peach firmness using a novel acoustic real-time detection device and Vis/NIR technology

Firmness is a critical indicator for predicting fruit ripeness, optimal harvest date, and shelf life. In this study, a novel fruit acoustic real-time detection prototype device and a conventional visible near-infrared (Vis/NIR) spectroscopy real-time detection device were used to collect acoustic and spectral signals from yellow flesh peaches to jointly predict their firmness. The acoustic and optical signals were generated into one- and two-dimensional feature data by complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), continuous wavelet transform (CWT) and Gramian angular field (GAF) data processing methods. Based on these data, a variety of yellow flesh peach firmness prediction models were constructed in this study, including partial least square (PLS), support vector regression (SVR), Swin Transformer (SwinT), and SwinT-PLS/SVR. The experimental results showed that the SwinT-PLS model based on the fusion of competitive adaptive re-weighted sampling (CARS)-acoustic image features and CARS-Vis/NIR spectral features showed the best prediction performance (R2P = 0.951, the RMSEP = 0.443 N/mm, RPDP = 4.339), and the prediction performance is significantly higher than that of the prediction model based on single acoustic and Vis/NIR spectral data. The method proposed can fast, non-destructively, accurately predict fruit firmness and has excellent prospects for commercial real-time fruit sorting applications.

Research on vector bending sensors based on taper-drawn seven-core fiber Bragg grating

We present a novel vector bending sensor that enables simultaneous measurement of the bending response in six outer cores of a seven-core fiber (SCF) without the need for fan-in/fan-out configurations. Utilizing femtosecond laser technology, we inscribe seven Fiber Bragg Gratings (FBGs) with distinct wavelengths across the SCF cores. The sensor’s tapering process, tailored for the bending sensing scenario, effectively combines the reflection peaks of the FBGs into one detection channel, thereby substantially improving the measurement efficiency. Our experimental results demonstrate a strong directional dependence of the bending response, with a maximum sensitivity of 127 pm/m−1. The bending angle and curvature magnitude are reconstructed by analyzing the wavelength shifts of any two non-diagonal outer cores, offering a versatile solution for real-time monitoring applications. This sensor design, by eliminating the need for additional fan-in/fan-out devices, simplifies the system architecture and reduces both measurement time and sensor size, making it highly suitable for applications in precision manufacturing, environmental monitoring, and robotics.

Prediction of Expanded Disability Status Scale in patients with MS using deep learning

Multiple sclerosis (MS) is a chronic neurological condition that leads to significant disability in patients. Accurate prediction of disease progression, specifically the Expanded Disability Status Scale (EDSS), is crucial for personalizing treatment and improving patient outcomes. This study aims to develop a robust deep neural network framework to predict EDSS in MS patients using MRI scans. Our model demonstrates high accuracy and reliability in both lesion segmentation and disability classification tasks. For segmentation, the model achieves a Dice Coefficient of 0.87, a Jaccard Index of 0.79, sensitivity of 0.85, and specificity of 0.88. In classification, it attains an overall accuracy of 91.2 %, with a precision of 0.89, recall of 0.88, and an F1-Score of 0.885. Ablation studies highlight the significant impact of integrating T2-weighted and FLAIR images, improving accuracy from 85.7 % (T1-weighted alone) to 93.4 %. Comparative analysis with state-of-the-art methods demonstrates our model's superiority, outperforming Method A and Method B in both accuracy and F1-Score. Despite these advancements, challenges such as data quality, sample size, and computational complexity remain. Future research should focus on standardizing imaging protocols, incorporating larger and more diverse datasets, and optimizing model efficiency. Advancing deep learning architectures and utilizing multimodal data can enhance predictive power and facilitate real-time clinical applications. Our study significantly contributes to refining MS treatment strategies by providing a comprehensive evaluation of our model's performance and addressing key limitations. Accurate disability predictions enable personalized treatments, early interventions, and improved patient outcomes, thus enhancing the quality of life for individuals affected by MS.

Development of an external system for monitoring the couch speed in radiotherapy using continuous bed movement.

Total body irradiation before bone marrow transplantation for hematological malignancies using Radixact, a high-precision radiotherapy machine, can potentially reduce side effects and the risk of secondary malignancies. However, stable control of couch speed is critical, and direct assessment methods outlined in quality assurance guidelines are lacking. This study aims to develop a real-time couch speed verification system for the Radixact. The developed system used a linear encoder to measure couch speed directly. Accuracy was verified via a linear stage, comparing measurements with a laser distance sensor. After placing a phantom simulating the human body on the Radixact couch, the couch speed was verified using predefined speed plans. Operating the linear stage at 0.1, 0.5, and 1.0mm/s revealed that the maximum position error of the developed verification system compared to the laser distance sensor was nearly equivalent to the distance resolution of the system (0.05mm/pulse), with negligible average speed error. When the Radixact couch operated at 0.1, 0.5, and 1.0mm/s, the values obtained by the verification system agreed with the theoretical values within the sampling period (0.01s) and distance resolution (0.05mm). The verification system developed provides real-time monitoring of the speed of the Radixact table, ensuring treatment effectiveness and patient safety. It would guarantee the couch speed's soundness and contribute to the "visualization" of safety.

Developing a real-time hand exoskeleton system that controlled by a hand gesture recognition system via wireless sensors

Numerous studies have been conducted using wearable sensors on gesture recognition and classification methods for the control of robotic arms, prostheses, and exoskeleton systems. The primary goal of this study is to classify 32 different hand gestures in real-time using electromyography (EMG) signals. EMG signals obtained by measuring the electrical activity of the muscles were simulated wirelessly on a hand exoskeleton. A Myo Armband placed on the upper arm was used to generate these signals for evaluating and validating the system. The signals were normalized according to maximum voluntary contraction (MVC), and relevant features such as MAV, STD, VAR, RMS, IEMG, ZC, and WL were selected using the univariate feature selection. A support vector machine (SVM) was then used for classification. The 32 different hand gestures were classified to an accuracy level of 99.9%. In short, this proposed approach can be effectively used in real-time hand gesture recognition and generating real-time actions for the hand exoskeleton system.

Comparative analysis of Vedic multiplier using Vedic sutras with existing multipliers in biomedical application

In today's computer world, a lot of real-time applications require rapid processing units. Arithmetic Logic Units (ALU) and Multiply-Accumulate (MAC) are the fundamental parts of these circuits and are necessary for their effective and rapid operation. The most significant prevalent part of digital signal processing devices is multipliers. The multiplier, adder, and registers need to be changed in order to maintain accuracy and increase execution speed, which will improve the performance of the ALU and MAC. The development of greater multipliers is being given priority for application in processors because of the increasing constraints on latency. To accelerate multiplication, it is essential to develop quicker multipliers. Vedic multipliers are preferred over different current expansions due to their low power consumption, fast operation, and efficient use of space. Vedic mathematics-based algorithms are often utilized to build quick, low-power multipliers. In addition to simulation results, this section covers the four sutras of Vedic mathematics: Urdhva Tiryakbhyam, Ekadhikena Purvena, Ekanyunena Purvena, and Nikhilam. Vedic multipliers are also compared to a variety of modern multipliers, including booth, Wallace, and array multipliers. All of the sutras are evaluated according to area, speed, power, propagation delay, and mean relative error (MRE) in the current research. The results of the study will be applied in the biomedical field.

Using heat map data to measure population development trends and urban disparities of large-scale affordable communities in China

Over the past decade, large-scale affordable communities have proliferated in the suburban areas of major Chinese cities. Most of the housing in these communities consists of affordable housing provided by the government and constructed by developers, targeted at middle to low-income individuals through restricted-price sales and rentals. With more than a decade of occupancy, it is crucial to examine the development status of these communities. This study employs regular collection of heat map data to observe the population density trends in communities from 2020 to 2024. By analyzing population density and its derived variables, the study examines changes in daytime and nighttime populations during weekdays, as well as the differences between the Spring Festival period and regular population data. Combining urban context, we use both quantitative and qualitative methods to analyze population quantity and structure trends and further explore the development disparities among cities. The study reveals a widespread increase in population across 35 communities in five cities from 2020 to 2021, peaking in 2021 and 2022, with differentiation among cities after 2023. Before 2022, there was a pattern in the development of community population quantity and structure: communities with faster population growth exhibited a simultaneous increase in commuter proportions. The impetus for community population changes is largely attributed to the varying attractiveness of cities to the floating population, with the five cities demonstrating distinct patterns. The method used in this study to collect population data, as opposed to traditional questionnaire surveys, presents advantages in terms of real-time, efficiency, convenience, and high sensitivity. This approach can offer feasible insights for establishing a real-time monitoring system for the development of large-scale affordable communities in China in the future.

Improving preparation in the emergency trauma room: the development and impact of real-time data transfer and dashboard visualization system.

This study examines, with clinical end users, the features of a visualization system in transmitting real-time patient data from the ambulance to the emergency trauma room (ETR) to determine if the real-time data provides the basis for more informed and timely interventions in the ETR before and after patient arrival. We conducted a qualitative in-depth interview study with 32 physicians in six German and Swiss hospitals. A visualization system was developed as prototype to display the transfer of patient data, and it serves as a basis for evaluation by the participating physicians. The prototype demonstrated the potential benefits of improving workflow within the ETR by providing critical patient information in real-time. Physicians highlighted the importance of features such as the ABCDE scheme and vital signs that directly impact patient care. Configurable and mobile versions of the prototype were suggested to meet the specific needs of each clinic or specialist, allowing for the transfer of only essential information. The results highlight on the one hand the potential need for adaptable interfaces in medical communication technologies that balance efficiency with minimizing additional workload for emergency medical services and show that the use of pre-notification systems in communication between ambulance and hospital can be supportive. Further research is recommended to assess practical application and support in clinical practice, including a re-evaluation of the enhanced prototype by professionals.

Developed square-root cubature Kalman filter-based solution for improving power system state estimation with unknown inputs and non-Gaussian noise

Understanding the ever-changing dynamics of power systems is crucial, and dynamic state estimation (DSE) plays a vital role in achieving this. However, traditional nonlinear Kalman filters (NKFs) face limitations: lack of access to control inputs and presence of non-Gaussian noise in measurements, impacting their accuracy and robustness. This research introduces a novel robust DSE method that tackles these challenges head-on. For the first time in DSE, it leverages the predictive power of Holt-Winters Triple Exponential Smoothing to model the time-varying behavior of control inputs. This innovative approach allows for the simultaneous estimation of dynamic state variables such as the rotor angle and rotor speed changes, as well as transient voltages and control inputs like mechanical input torque and excitation voltage, even in the presence of non-Gaussian noise. Furthermore, the method employs modified projection statistics and a Cauchy function. This unique combination effectively bounds the influence of observation outliers while maintaining high statistical estimation efficiency. This innovative approach utilizes a square cubature Kalman filter (SCKF) for enhanced numerical stability. Extensive simulations under various anomalous conditions demonstrate the method's superior accuracy and efficiency in estimating the state vector. These results highlight its potential to significantly improve power system estimation and pave the way for real-time applications.

Improving surveillance of emergency department activity through natural language processing

Objective and ApproachManagers, clinicians and policymakers have limited information on near-real time patterns of attendance at emergency departments (ED). Our aim was to develop a surveillance system through extracting information from the presenting complaint to develop and feed activity dashboards, improve injury surveillance and support research. We developed a Natural Language Processing (NLP) pipeline using the General Architecture for Text Engineering (GATE) system. The pipeline produced Unified Medical Language System (UMLS) codes from ED presenting complaint records and also implemented the Joint Action on Monitoring Injuries in Europe (JAMIE) Minimum Dataset. We classified complaints into 31 common problem presentations (Coughs, Bleeds, Mental Health, Dizziness...), along with categories from JAMIE (Mechanism of Injury, Location, Activity, Intent…). ResultsPreliminary results are from a pilot study of 86,416 presenting complaints from Morriston Hospital in 2014. The most common terms used for grouped symptoms/categories were “Injury” (38.6%, n=28691), “Pain” (22.8%, n=19420), “Unwell” (7.2%, n=6117), “Bleeding” (6.7%, n=5760), and “Shortness of Breath” (3.8%, n=3255). 32.3% of records contained Mechanism of Injury codes, including, falls (15.2%, n=13174), struck by/against (6.9%, n=5960), cuts (5.6%, n=4833), road traffic collisions (2.5%, n=2124) and poisoning (1%, n=900). We are continuing to refine and validate the categories using precision and recall and with comparison with coded diagnoses. Conclusions and Implications NLP can be used to extract valuable additional detailed information from ED data. We will further develop the algorithms for all hospitals in Wales for data from 2023 and design dashboards to produce a near-real time surveillance system.

Fourier-based beamforming for 3D plane wave imaging and application in vector flow imaging using selective compounding

Objective. Ultrafast ultrasound imaging using planar or diverging waves for transmission is a promising approach for efficient 3D imaging with matrix arrays. This technique has advantages for B-mode imaging and advanced techniques, such as 3D vector flow imaging (VFI). The computation load of the cross-beam technique is associated with the number of transmit anglesmand receive anglesn. The full velocity vector is obtained using the least square fashion. However, the beamforming is repeatedm × ntimes using a conventional time-domain delay-and-sum (DAS) beamformer. In the 3D case, the collection and processing of data from different beams increase the amount of data that must be processed, requiring more storage capacity and processing power. Furthermore, the large computation complexity of DAS is another major concern. These challenges translate into longer computational times, increased complexity in data processing, and difficulty in real-time applications.Approach. In response to this issue, this study proposes a novel Fourier domain beamformer for 3D plane wave imaging, which significantly increases the computational speed. Additionally, a selective compounding strategy is proposed for VFI, which reduces the beamforming process fromm × ntom(wheremandnrepresent the number of transmission and reception, respectively), effectively shortening the processing time. The underlying principle is to decompose the receive wavefront into a series of plane waves with different slant angles. Each slant angle can produce a sub-volume for coherent or selective compounding. This method does not rely on the assumption that the plane wave is perfect and the results show that our proposed beamformer is better than DAS in terms of resolution and image contrast. In the case of velocity estimation, for the Fourier-based method, only Tx angles are assigned in the beamformer and the selective compounding method produces the final image with a specialized Rx angle.Main results. Simulation studies andin vitroexperiments confirm the efficacy of this new method. The proposed beamformer shows improved resolution and contrast performance compared to the DAS beamformer for B-mode imaging, with a suppressed sidelobe level. Furthermore, the proposed technique outperforms the conventional DAS method, as evidenced by lower mean bias and standard deviation in velocity estimation for VFI. Notably, the computation time has been shortened by 40 times, thus promoting the real-time application of this technique. The efficacy of this new method is verified through simulation studies andin vitroexperiments and evaluated by mean bias and standard deviation. Thein vitroresults reveal a better velocity estimation: the mean bias is 2.3%, 3.4%, and 5.0% forvx,vy, andvz, respectively. The mean standard deviation is 1.8%, 1.7%, and 3.4%. With DAS, the evaluated mean bias is 9.8%, 4.6%, and 6.7% and the measured mean standard deviation is 7.5%, 2.5%, and 3.9%.Significance. In this work, we propose a novel Fourier-based method for both B-mode imaging and functional VFI. The new beamformer is shown to produce better image quality and improved velocity estimation. Moreover, the new VFI computation time is reduced by 40 times compared to conventional methods. This new method may pave a new way for real-time 3D VFI applications.