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Directional Coherence Factor for Volumetric Ultrasound Imaging with Matrix Arrays

IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control 2025年第6期PP卷 PP页

作者： Wu, Xiaochuan Lee, Wei-Ning The University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong The University of Hong Kong Department of Electrical and Electronic Engineering and Biomedical Engineering Programme Hong Kong

Matrix arrays with small apertures limit spatial and contrast resolutions of volumetric ultrasound imaging. Coherence-based beamformers are prevalent for side lobe suppression and resolution improvement. While the spatial coherence of a matrix array is fundamentally a 2D function, conventional coherence factor (CF) methods neglect the directional variation of an M × N matrix array when calculating volumetric coherence. We hereby propose a projectionbased directional coherence factor (DCF) to exploit the 2D nature of volumetric coherence function. Instead of computing the coherent and incoherent summations across the entire 2D aperture, DCF projects aperture data onto azimuthal, elevational, diagonal, and antidiagonal directions and subsequently calculates the coherence factors for each direction separately. The orthogonal coherence pairs, i.e., azimuth and elevation, diagonal and anti-diagonal, are multiplied to obtain DCFRC and DCFDiag, respectively. The Jaccard similarity of the DCFRC and DCFDiag is used to derive the final DCF to weigh the reconstructed images. We evaluated the performance of DCF beamforming in point-target simulations, multi-purpose phantom experiments, and in vivo muscle imaging, and compared it to delay-and-sum (DAS) and CF beamformers. Our DCF achieved side lobe reduction throughout the entire volume compared to conventional CF. Moreover, diagonal weighting significantly improved, on average, the azimuthal resolution by 41.3% vs. DAS and 7.35% vs. CF as well as the elevational resolution by 40.4% vs. DAS and 38.7% vs. CF. Our proposed DCF offers a practical solution for resolution and contrast enhancement of volumetric imaging in 2D matrix array configurations. © 1986-2012 IEEE.

关键词： Ultrasonic imaging

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Seroprevalence of SARS-CoV-2 antibodies and demographic predictors among asymptomatic individuals in Osun State, Nigeria

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Discover Public Health 2025年第1期22卷 1-9页

作者： Oyetunde, Oluwapelumi Breakthrough Alao, Jude Oluwapelumi Akinsola, Oluwatomisin Aderonke Awobiyi, Hezekiah Oluwajoba Aremu, Rasidat Oyindamola Ajimoh, Oreoluwa Deborah Lawal, Khadijah Dolapo Oluwasanya, Glory Jesudara Abiosun, Olanike Moyoloye Oladipo, Elijah Kolawole Biomedical Health Department College of Science and Engineering University of Derby Derby England School of Public Health and Interdisciplinary Studies Auckland University of Technology Auckland New Zealand Microbiology Programme Engineering and Science College of Agric Bowen University Iwo Nigeria Faculty of Natural Science Redeemer’s University Ede Nigeria Microbiology Department Adeleke University Ede Nigeria School of Life Sciences University of Essex Essex England

This study assesses the seroprevalence of SARS-CoV-2 antibodies (IgM, IgG, and IgA) among asymptomatic and unvaccinated individuals in Osun State, Nigeria, to quantify hidden infections and inform targeted public health interventions. A total of 300 participants from diverse socio-demographic backgrounds were tested using ELISA in this cross-sectional study, conducted between February and May 2021. The study revealed a seroprevalence of 58.0%, with notable differences between urban and rural areas, emphasizing the influence of urbanization on virus transmission. No significant associations were found between seropositivity and demographic factors such as age, gender, or occupation, indicating widespread transmission across all groups. However, elevated seroprevalence was observed among married individuals and self-employed workers, suggesting higher exposure in household and occupational settings. These findings underscore the urgent need for tailored public health strategies in low- and middle-income countries, with a focus on urban centers, reducing household transmission, and safeguarding informal workers. The alignment of these results with global trends highlights the critical role of local data in shaping effective public health responses. This study calls for targeted interventions, continuous surveillance, and comparative studies to better manage the current pandemic and prepare for future public health crises. The research provides valuable insights into controlling COVID-19 transmission in similar settings, with significant implications for global health strategies.

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The impact of virtual reality on private value dimensions: a healthcare case study

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Public Management: An International Journal of Research and Theory 2025年第5期27卷

作者： Higor Leite Biomedical Engineering Postgraduate Research Programme - School of Management and Economics Federal University of Technology Paraná – Brazil Curitiba Brazil

ABSTRACTThe digitalization of public services is a growing phenomenon that affects providers and users alike. However, the impact of digitalization on the Public Service Logic remains understudied. We aim to understand how virtual reality impacts private value dimensions in public healthcare. Drawn from a dual-perspective of providers’ and users’ interviews and personal diaries, we contribute to value-in-context by underlining nuances of the user needs and service outcomes. Similarly, value-in-use shows patients’ experimentations and experiences with the virtual reality service. This study contributes to value dimensions, capturing the phenomenological aspects of the public service encounter in a complex digital healthcare ecosystem.

关键词： Digitalization public health service private value public service logic virtual reality value destruction

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Genetic variations in human opsin genetics: Understanding color vision phenotypes and defects

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ACTA OPHTHALMOLOGICA 2025年第S284期103卷

作者： Lilja, Olivia Backlund, Michael Tiihonen, Jussi Kangas, Harri Donner, Kati Ala-Laurila, Petri Turunen, Joni Eye Genetics Group Folkhälsan Research Center Helsinki Finland Department of Neuroscience and Biomedical Engineering Aalto University Espoo Finland Molecular and Integrative Biosciences Research Programme University of Helsinki Finland Institute for Molecular Medicine Finland (FIMM) University of Helsinki Helsinki Finland Department of Ophthalmology University of Helsinki and Helsinki University Hospital Helsinki Finland

Purpose: The aim of this study was to analyze human opsin genes, OPN1LW and OPN1MW , for single nucleotide polymorphisms (SNP) and structural variants (SV) impacting color vision in subjects diagnosed with color vision deficiency. Methods: Nineteen subjects underwent anomaloscope examination to diagnose color vision deficiency phenotypes (dichromatic: protanopia and deuteranopia). First, a previously published method of long range-PCR and sequencing was performed to identify variations in OPN1LW , OPN1MW , and OPN1SW . In the case of discrepancy, ultra-long nanopore sequencing was utilized to identify possible SVs. Results: The long range-PCR and sequencing genetically validated the color vision diagnosis for 17 of the 19 individuals. For two individuals the genetic and functional diagnoses conflicted. Ultra-long nanopore sequencing of these two revealed possible rearrangements in the opsin gene cluster that could affect the color vision and explain the conflicting results based on the long-PCR method. Conclusions: The large size of the human opsin gene cluster and the high degree of sequence homology between OPN1LW and OPN1MW genes have been a challenge for genetic studies of color vision deficiencies. The development of ultra-long nanopore sequencing technology allows for a more comprehensive genetic analysis of this region, especially for the detection of pathogenic SVs. In this study, we were able to identify possible SVs underlying color vision deficiency in cases that were missed by the long range-PCR. However, alignment of the ultra-long sequences to the reference genome at the variable opsin gene cluster region remains a challenge for the data analysis.

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Automated Microbubble Discrimination in Ultrasound Localization Microscopy by Vision Transformer

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IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control 2025年 PP卷 PP页

作者： Wang, Renxian Lee, Wei-Ning The University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong The University of Hong Kong Department of Electrical and Electronic Engineering Biomedical Engineering Programme Hong Kong

Ultrasound localization microscopy (ULM) has revolutionized microvascular imaging by breaking the acoustic diffraction limit. However, different ULM workflows depend heavily on distinct prior knowledge, such as the impulse response and empirical selection of parameters (e.g., the number of microbubbles (MBs) per frame M), or the consistency of training-test dataset in deep learning (DL)-based studies. We hereby propose a general ULM pipeline that reduces priors. Our approach leverages a DL model that simultaneously distills microbubble signals and reduces speckle from every frame without estimating the impulse response and M. Our method features an efficient channel attention vision transformer (ViT) and a progressive learning strategy, enabling it to learn global information through training on progressively increasing patch sizes. Ample synthetic data were generated using the k-Wave toolbox to simulate various MB patterns, thus overcoming the deficiency of labeled data. The ViT output was further processed by a standard radial symmetry method for sub-pixel localization. Our method performed well on model-unseen public datasets: one in silico dataset with ground truth and four in vivo datasets of mouse tumor, rat brain, rat brain bolus, and rat kidney. Our pipeline outperformed conventional ULM, achieving higher positive predictive values (precision in DL, 0.88-0.41 vs. 0.83-0.16) and improved accuracy (root-mean-square errors: 0.25-0.14 λ vs. 0.31-0.13 λ) across a range of signal-to-noise ratios from 60 dB to 10 dB. Our model could detect more vessels in diverse in vivo datasets while achieving comparable resolutions to the standard method. The proposed ViT-based model, seamlessly integrated with state-of-the-art downstream ULM steps, improved the overall ULM performance with no priors. © 1986-2012 IEEE.

关键词： Point spread function progressive learning speckle reduction super-resolution ultrasound localization microscopy vision transformer

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Indonesian text-to-speech using syllable concatenation for PC-based low vision aid

Indonesian text-to-speech using syllable concatenation for P...

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2011 International Conference on Electrical engineering and Informatics, ICEEI 2011

作者： Soedirdjo, Subaryani D.H. Zakaria, Hasballah Mengko, Richard Biomedical Engineering Programme Institut Teknologi Bandung Ganesha 10 Bandung Indonesia

ISBN: (纸本)9781457707520

Severe low vision patient cannot read a text clearly. Thus, an instrument engineered for them should have an extra function to read aloud the text. Nowadays, Bahasa Indonesia is still not supported properly in any text to speech modul. This ongoing research is trying to develop an Indonesian text to speech with syllable concatenation. The input of the system comes from a plain text file or manually typed input. This text is then truncated until we get a list of words. The word is then analyzed using brute force algorithm to get proper combination of syllable according to the sound library, which has been recorded previously. Arranged sound then came out from speaker. Test of the system shows that this method works well for any text in Bahasa Indonesia. © 2011 IEEE.

关键词： Bahasa Indonesia Bahasa Indonesia Delay Force Indonesian text-to-speech module Instruments Libraries PC-based low vision aid Software Speech Visualization brute force algorithm handicapped aids plain text file speech synthesis syllable concatenation syllable concatenation text analysis text to speech vision defects

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Production of recombinant collagen: State of the art and challenges

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engineering Biology 2017年第1期1卷 18-23页

作者： Wang, Tianyi Lew, Jiewei Premkumar, Jayaraman Poh, Chueh Loo Naing, May Win Bio-Manufacturing Programme Singapore Institute of Manufacturing Technology Singapore Department of Biomedical Engineering National University of Singapore Singapore

Collagen, which is often used in healthcare materials and biomedical research, is largely extracted from animal sources. Recombinant human collagen has the potential to be a promising alternative to animal collagen which has many shortcomings, including immunogenicity and lack of biocompatibility. Currently, recombinant human collagen has been expressed in both eukaryotic and prokaryotic hosts with varying degrees of success. One issue with recombinant collagen across all hosts is the inability to achieve full length collagen with native amounts of post-translational modifications, prompting much exciting research in this direction. There has also been much effort in improving yield and biomimicry of recombinant collagen. This review discusses collagen structure and current methods for extracting animal collagen, before introducing current research in synthesising recombinant human collagen in various hosts, and finally highlighting challenges in the field. © 2017 Eng. Biol. All rights reserved.

关键词： Collagen

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Development of EEG-based epileptic detection using artificial neural network

Development of EEG-based epileptic detection using artificia...

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2012 International Conference on biomedical engineering, ICoBE 2012

作者： Abdullah, Azian Azamimi Rahim, Saufiah Abdul Ibrahim, Adira Biomedical Electronics Engineering Programme School of Mechatronic Engineering University Malaysia Perlis 02600 Ulu Pauh Malaysia

ISBN: (纸本)9781457719899

Epilepsy is one of the most common neurological disorders causing from repeating brain seizures that are the result of the temporal and sudden electrical disturbance of the brain. Electroencephalogram (EEG) signal plays an important role in the diagnosis of epilepsy. This project proposed to develop a system that can detect epilepsy based on EEG signal using artificial neural network. Discrete Wavelet Transform (DWT) and Fast Fourier Transform (FFT) were applied as feature extraction methods. These features then set as input to the feedforward neural network with backpropagation training algorithm to get the classification accuracy. The accuracy of DWT with 10000 epochs is 97% while accuracy of FFT method gives 53.889% accuracy. The combination of DWT and FFT extracted features give the highest accuracy, which is 98.889%. The classification accuracy depends on the number of epoch and the features from the feature extraction. Increased number of epoch gives long response time to train the network. © 2012 IEEE.

关键词： Discrete wavelet transforms

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Prediction of Alzheimer's disease (AD) Using Machine Learning Techniques with Boruta Algorithm as Feature Selection Method

Prediction of Alzheimer's disease (AD) Using Machine Learnin...

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2019 International Conference on biomedical engineering, ICoBE 2019

作者： Leong, Lee Kuok Abdullah, Azian Azamimi Biomedical Electronic Engineering Programme School of Mechatronic Engineering Universiti Malaysia Perlis Perlis-Arau02600 Malaysia

Alzheimer's disease (AD) is the most frequent incurable neurodegenerative disease, a general term for memory loss and other cognitive abilities. Early detection of AD can help with proper treatment and prevent brain tissue damage. Traditional medical tests are time consuming, fail to recognize early signs and lack of diagnosis sensitivity and specificity. To achieve promising prediction accuracy, the best predictive machine learning model is selected based on initial pre-processing step followed by vital attributes selection and performance evaluation for five proposed supervised machine learning algorithms. In the pre-processing, all the missing values have been removed since the overall percentage only covered 5.63%. Boruta algorithm as feature selection method resulted Atlas Scaling Factor, Estimated Total Intracranial Volume, Normalized Whole-brain Volume, Mini-Mental State Examination and Clinical Dementia Rating must be included as primary features. With Boruta algorithm, it has been shown that Random Forest Grid Search Cross Validation (RF GSCV) outperformed with 94.39% of accuracy, 88.24% sensitivity, 100.00% specificity and 94.44% AUC among other 12 models that includes conventional and fine-tuned models even for the small OASIS-2 longitudinal MRI dataset. Finally, our developed Graphical User Interface (GUI) prediction tool was evaluated through prediction over OASIS-1 cross-sectional MRI dataset containing 216 samples of imaging sessions that have been pre-processed. Prediction results were closed with the dementia status provided in OASIS cross-sectional data fact sheet. © 2019 IOP Publishing Ltd. All rights reserved.

关键词： Forecasting

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High-precision Rhythm-based Detection of Atrial Fibrillation and Frequent Premature Contractions using Extreme Gradient Boosting 15

High-precision Rhythm-based Detection of Atrial Fibrillation...

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15th biomedical engineering International Conference, BMEiCON 2023

作者： Tearwattanarattikal, Tanawan Lek-Uthai, Apiwat Chulalongkorn University Interdisciplinary Programme of Biomedical Engineering Bangkok Thailand Chulalongkorn University Department of Electrical Engineering Bangkok Thailand

ISBN: (纸本)9798350345247

Atrial fibrillation and premature contractions are two of the most common types of arrhythmia, potentially leading to serious medical complications, such as stroke and heart failure. Normally, these two arrhythmias are detected by inspecting the electrocardiogram (ECG) which requires medical expertise, making it unsuitable for home-based monitoring used by patients with no medical background. This research aimed to develop a rhythm-based algorithm to detect atrial fibrillation and frequent premature contractions with at least 20% burden. The algorithm was trained using ECG data from publicly available MIT-BIH Arrhythmia Database and Long-Term AF Database divided into 30-second-long ECG segments. After QRS detection, relevant features, involving heart rate variability, Shannon's entropy, and sample entropy, were extracted from the RR interval and classified using state-of-the-art extreme gradient boosting (XGBoost). The final model achieved the performance of 98.10% F1-score on an independent test set. Thus, this algorithm can be applied on home-based monitoring which can be used as a screening intervention for the healthcare system. © 2023 IEEE.

关键词： Electrocardiograms

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