IEEE Transactions on Biomedical Engineering最新文献_第9页

Quantification of Lung Stiffness Using Magnetic Resonance Elastography (MRE): Clinical Validation for Smokers. 使用磁共振弹性成像（MRE）量化肺刚度：吸烟者的临床验证。

IF 4.4 2区医学

IEEE Transactions on Biomedical Engineering Pub Date : 2025-03-20 DOI: 10.1109/TBME.2025.3553375

Sabine F Bensamoun, Kiaran P McGee, Mashhour Chakouch, Philippe Pouletaut, Fabrice Charleux

{"title":"Quantification of Lung Stiffness Using Magnetic Resonance Elastography (MRE): Clinical Validation for Smokers.","authors":"Sabine F Bensamoun, Kiaran P McGee, Mashhour Chakouch, Philippe Pouletaut, Fabrice Charleux","doi":"10.1109/TBME.2025.3553375","DOIUrl":"10.1109/TBME.2025.3553375","url":null,"abstract":": Tobacco-related pathologies are the most preventable diseases. The purpose is to provide personalized cartography of smoker lung stiffness using non-irradiating imaging modalities, MRI and MRE (magnetic resonance imaging/elastography).Methods: Thirty-four smokers were divided into five groups distributed with a range of pack-years (PY) of 10. All patients underwent three imaging tests (CT: computed tomography, MRI, MRE) to make possible measurements of lung density, with two modalities (CT, MR), and stiffness. CT lung density was measured using the Hounsfield number. MR density was obtained from a fast gradient echo sequence and validated with an in vitro 3D abdominal phantom. The MRE test was performed with a motion-encoding gradient (Z direction), a spin-echo echo-planar sequence and four offsets. A pneumatic driver (frequency: 50 Hz) was placed on the right lung and four axial phase images were recorded. Post-processing was then performed to record a personalized stiffness cartography.Results: CT density significantly increased in relation to PY, showing denser tissue for the heavy smokers. As MR density acquisition is less accurate than CT, a slight increase in lung density was obtained. MRE tests revealed a significant increase in stiffness with pack-year. Patient-specific lung stiffness showed inhomogeneous distribution of values.Conclusion: MRE could provide a personalized cartography of stiffness for regular uptake of the lung's mechanical behavior in smokers. The stiffness could become a biomarker for preventing future lung disease.Significance: MRE test could be an alternative to CT test for the follow-up of smokers.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143669736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IEEE Transactions on Biomedical Engineering Information for Authors IEEE生物医学工程信息汇刊作者

IF 4.4 2区医学

IEEE Transactions on Biomedical Engineering Pub Date : 2025-03-20 DOI: 10.1109/TBME.2025.3564249

引用次数: 0

IEEE Transactions on Biomedical Engineering Handling Editors Information IEEE生物医学工程学报编辑信息处理

IF 4.4 2区医学

IEEE Transactions on Biomedical Engineering Pub Date : 2025-03-20 DOI: 10.1109/TBME.2025.3542033

引用次数: 0

IEEE Engineering in Medicine and Biology Society Information IEEE医学与生物工程学会信息

IF 4.4 2区医学

IEEE Transactions on Biomedical Engineering Pub Date : 2025-03-20 DOI: 10.1109/TBME.2025.3542029

引用次数: 0

GZSL-Lite: A Lightweight Generalized Zero-Shot Learning Network for SSVEP-Based BCIs. GZSL-Lite：基于ssvep的bci的轻量级广义零射击学习网络。

IF 4.4 2区医学

IEEE Transactions on Biomedical Engineering Pub Date : 2025-03-20 DOI: 10.1109/TBME.2025.3553204

Xietian Wang, Aiping Liu, Heng Cui, Xingui Chen, Kai Wang, Xun Chen

{"title":"GZSL-Lite: A Lightweight Generalized Zero-Shot Learning Network for SSVEP-Based BCIs.","authors":"Xietian Wang, Aiping Liu, Heng Cui, Xingui Chen, Kai Wang, Xun Chen","doi":"10.1109/TBME.2025.3553204","DOIUrl":"10.1109/TBME.2025.3553204","url":null,"abstract":"Generalized zero-shot learning (GZSL) networks offer promising avenues for the development of user-friendly steady-state visual evoked potential (SSVEP) based brain-computer interfaces (BCIs), aiming to alleviate the training burden on users. These networks only require the user to provide training data from partial classes during training, yet they demonstrate the capability to classify all classes during testing. However, these GZSL networks have a large number of trainable parameters, resulting in long training times and difficulty to practicalize. In this study, we proposed a dual-attention structure to construct a lightweight GZSL network, termed GZSL-Lite. We first embedded the input training data-constructed class templates, manually constructed sine templates, and electroencephalogram (EEG) signals using convolution-based networks. The embedding part uses the same network weights to embed the features across different stimulus frequencies while reducing the depth of the network. After embedding, two branches of the dual-attention use class and sine templates to guide the feature extraction of the EEG signal with the attention mechanism, respectively. Compared to the networks that extract all features equally, dual-attention focuses only on EEG features relative to templates, which helps classification with fewer parameters. Finally, we used depthwise convolutional blocks to output classification results. Experimental evaluations conducted on two publicly available datasets demonstrate the efficacy of the proposed network. Comparative analysis reveals a remarkable reduction in trainable parameters to less than 1% of the SOTA counterpart, concurrently showing significant performance improvement. The code is available for reproducibility at https://github.com/xtwong111/GZSL-Lite-for-SSVEP-Based-BCIs.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143669732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Population-Driven Synthesis of Personalized Cranial Development from Cross-Sectional Pediatric CT Images. 儿童横断面CT图像中人口驱动的个性化颅骨发育综合。

IF 4.4 2区医学

IEEE Transactions on Biomedical Engineering Pub Date : 2025-03-18 DOI: 10.1109/TBME.2025.3550842

Jiawei Liu, Fuyong Xing, Connor Elkhill, Marius George Linguraru, Randy C Miles, Ines A Cruz-Guerrero, Antonio R Porras

{"title":"Population-Driven Synthesis of Personalized Cranial Development from Cross-Sectional Pediatric CT Images.","authors":"Jiawei Liu, Fuyong Xing, Connor Elkhill, Marius George Linguraru, Randy C Miles, Ines A Cruz-Guerrero, Antonio R Porras","doi":"10.1109/TBME.2025.3550842","DOIUrl":"10.1109/TBME.2025.3550842","url":null,"abstract":"Objective: Predicting normative pediatric growth is crucial to identify developmental anomalies. While traditional statistical and computational methods have shown promising results predicting personalized development, they either rely on statistical assumptions that limit generalizability or require longitudinal datasets, which are scarce in children. Recent deep learning methods trained with cross-sectional dataset have shown potential to predict temporal changes but have only succeeded at predicting local intensity changes and can hardly model major anatomical changes that occur during childhood. We present a novel deep learning method for image synthesis that can be trained using only cross-sectional data to make personalized predictions of pediatric development.Methods: We designed a new generative adversarial network (GAN) with a novel Siamese cyclic encoder-decoder generator architecture and an identity preservation mechanism. Our design allows the encoder to learn age- and sex-independent identity-preserving representations of patient phenotypes from single images by leveraging the statistical distributions in the cross-sectional dataset. The decoder learns to synthesize personalized images from the encoded representations at any age.Results: Trained using only cross-sectional head CT images from 2,014 subjects (age 0-10 years), our model demonstrated state-of-the-art performance evaluated on an independent longitudinal dataset with images from 51 subjects.Conclusion: Our method can predict pediatric development and synthesize temporal image sequences with state-of-the-art accuracy without requiring longitudinal images for training.Significance: Our method enables the personalized prediction of pediatric growth and longitudinal synthesis of clinical images, hence providing a patient-specific reference of normative development.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143656923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Camera Seismocardiogram Based Monitoring of Left Ventricular Ejection Time. 基于相机地震心动图的左心室射血时间监测。

IF 4.4 2区医学

IEEE Transactions on Biomedical Engineering Pub Date : 2025-03-18 DOI: 10.1109/TBME.2025.3548090

Zhiqin Zhou, Jia Huang, Haozhe Li, Lin Liu, Yingen Zhu, Caifeng Shan, Wenjin Wang

{"title":"Camera Seismocardiogram Based Monitoring of Left Ventricular Ejection Time.","authors":"Zhiqin Zhou, Jia Huang, Haozhe Li, Lin Liu, Yingen Zhu, Caifeng Shan, Wenjin Wang","doi":"10.1109/TBME.2025.3548090","DOIUrl":"10.1109/TBME.2025.3548090","url":null,"abstract":"Left Ventricular Ejection Time (LVET), reflecting the duration from the onset to the end of blood ejection by the left ventricle during each heartbeat, is a critical parameter for measuring cardiac pumping efficiency. Continuous and regular monitoring of LVET is particularly crucial in assessing cardiac health, valvular function, and myocardial contractility. Seismocardiogram (SCG) signals can be utilized for LVET monitoring, as the temporal distance between the aortic valve opening (AO) and aortic valve closure (AC) in SCG signals can accurately depict LVET. This study proposes a novel way to extract LVET from laser speckle videos recorded by a remote camera based on the principle of defocused speckle imaging, thereby enabling non-contact monitoring of LVET. We extract both the low-frequency components of laser speckle motion (LSM-LF), regarded as SCG signals, and the high-frequency components of laser speckle motion (LSM-HF) from recorded videos. We utilize LSM-HF to assist the detection of AO and AC markers in LSM-LF. We validated the effectiveness of our AO and AC detection algorithm on a self-made dataset comprising 21 participants with 9616 SCG cycles. The benchmark shows that the detection accuracy for AO and AC reached 98.16% and 97.94%, respectively, with an mean absolute error of 0.5571 ms for LVET estimation. The results demonstrate that camera-SCG has strong potential for cardiac health monitoring.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143656833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

In Vivo Characterization of Central Arterial Properties Using a Miniaturized pMUT Array Compared to a Clinical Transducer: A Feasibility Study Towards Wearable Pulse Wave Imaging. 与临床传感器相比，使用微型 pMUT 阵列对中心动脉特性进行体内表征：实现穿戴式脉冲波成像的可行性研究

IF 4.4 2区医学

IEEE Transactions on Biomedical Engineering Pub Date : 2025-03-14 DOI: 10.1109/TBME.2025.3551281

Parth Gami, Tuhin Roy, Pengcheng Liang, Paul Kemper, Marco Travagliati, Leonardo Baldasarre, Stephen Bart, Elisa E Konofagou

{"title":"In Vivo Characterization of Central Arterial Properties Using a Miniaturized pMUT Array Compared to a Clinical Transducer: A Feasibility Study Towards Wearable Pulse Wave Imaging.","authors":"Parth Gami, Tuhin Roy, Pengcheng Liang, Paul Kemper, Marco Travagliati, Leonardo Baldasarre, Stephen Bart, Elisa E Konofagou","doi":"10.1109/TBME.2025.3551281","DOIUrl":"10.1109/TBME.2025.3551281","url":null,"abstract":"Objective: Piezoelectric micromachined ultrasound transducer (pMUT) technology shows promise for wearable ultrasound applications, although with limitations in acquisition performance compared to standard transducers. To translate Pulse Wave Imaging (PWI)-an ultrasound imaging technique that evaluates local arterial mechanics-into wearable applications, this study investigated the performance of integrating a miniaturized pMUT array into the PWI pipeline.Methods: Nine (n = 9) carotid arteries were scanned with a miniaturized pMUT array and an L7-4 linear transducer. Metrics like pulse wave velocity at end-diastole (PWVED) and end-systole (PWVES), compliance (CED, CES), and carotid pulse pressure (PPC) were compared between imaging arrays.Results: Lower SNR of axial wall velocities (SNRvPWI) at end-diastole (L7-4: 47.9 ± 6.8 dB, pMUT: 43.3 ± 7.4 dB) and end-systole (L7-4: 45.4 ± 6.4 dB, pMUT: 38.1 ± 6.5 dB), and trends of higher coefficient of variation (CV) were found for PWI performed with the pMUT array compared to the L7-4. Bland-Altman analysis identified good agreement between the L7-4 and pMUT array for average PWVED (bias = -0.02 ± 0.42 m/s), PWVES (bias = -0.38 ± 1.3 m/s), CED (bias = 0.04 x 10-9 ± 0.24 x 10-9 m2/Pa), CES (bias = 0.11 x 10-9 ± 0.38 x 10-9 m2/Pa) and PPC (bias = 1.06 ± 5.08 mmHg).Conclusion: The findings revealed comparable performance between the miniaturized pMUT array and L7-4 for PWI, highlighting the versatility of the PWI technique.Significance: This feasibility study illustrates the potential for translating PWI into wearable configurations, opening new avenues for cardiovascular health monitoring.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143630253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluation of a Low-Cost Amplifier With System Optimization in Thermoacoustic Tomography: Characterization and Imaging of Ex-Vivo and In-Vivo Samples. 热声断层成像系统优化的低成本放大器评价：离体和活体样品的表征和成像。

IF 4.4 2区医学

IEEE Transactions on Biomedical Engineering Pub Date : 2025-03-14 DOI: 10.1109/TBME.2025.3551260

Md Tarikul Islam, Mohsin Zafar, Ravi Prakash, Deepika Aggrawal, Danilo Erricolo, James Lin, Kamran Avanaki

{"title":"Evaluation of a Low-Cost Amplifier With System Optimization in Thermoacoustic Tomography: Characterization and Imaging of Ex-Vivo and In-Vivo Samples.","authors":"Md Tarikul Islam, Mohsin Zafar, Ravi Prakash, Deepika Aggrawal, Danilo Erricolo, James Lin, Kamran Avanaki","doi":"10.1109/TBME.2025.3551260","DOIUrl":"10.1109/TBME.2025.3551260","url":null,"abstract":"Microwave-induced thermoacoustic tomography (TAT) is a hybrid imaging technique that combines microwave excitation with ultrasound detection to create detailed images of biological tissue. Most TAT systems require a costly amplification system (or a sophisticated high-power microwave source), which limits the wide adoption of this imaging modality. We have developed a rotating single-element thermoacoustic tomography (RTAT) system using a low-cost amplifier that has been optimized in terms of microwave signal pulse width and antenna placement. The optimized system, enhanced with signal averaging, advanced signal processing, and a deep learning computational core, successfully produced adequate-quality images. The system has been characterized in terms of spatial resolution, imaging depth, acquisition speed, and multispectral capabilities utilizing tissue-like phantoms, ex-vivo specimens and in-vivo imaging. We believe our low-cost, portable system expands accessibility for the research community, empowering more groups to explore thermoacoustic imaging. It supports the development of advanced signal processing algorithms to optimize both low-power and even high-power TAT systems, accelerating the clinical adoption of this promising imaging modality.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143630249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Semi-Autonomous Laparoscopic Robot Docking with Learned Hand-Eye Information Fusion. 基于学习手眼信息融合的半自主腹腔镜机器人对接。

IF 4.4 2区医学

IEEE Transactions on Biomedical Engineering Pub Date : 2025-03-13 DOI: 10.1109/TBME.2025.3550974

Huanyu Tian, Martin Huber, Christopher E Mower, Zhe Han, Changsheng Li, Xingguang Duan, Christos Bergeles

{"title":"Semi-Autonomous Laparoscopic Robot Docking with Learned Hand-Eye Information Fusion.","authors":"Huanyu Tian, Martin Huber, Christopher E Mower, Zhe Han, Changsheng Li, Xingguang Duan, Christos Bergeles","doi":"10.1109/TBME.2025.3550974","DOIUrl":"10.1109/TBME.2025.3550974","url":null,"abstract":"In this study, we introduce a novel shared-control system for key-hole docking operations, combining a commercial camera with occlusion-robust pose estimation and a hand-eye information fusion technique. This system is used to enhance docking precision and force-compliance safety. To train a hand-eye information fusion network model, we generated a self-supervised dataset using this docking system. After training, our pose estimation method showed improved accuracy compared to traditional methods, including observation-only approaches, hand-eye calibration, and conventional state estimation filters. In real-world phantom experiments, our approach demonstrated its effectiveness with reduced position dispersion (1.230.81 mm vs. 2.47 1.22 mm) and force dispersion (0.780.57 N vs. 1.150.97 N) compared to the control group. These advancements in semi-autonomy co-manipulation scenarios enhance interaction and stability. The study presents an anti-interference, steady, and precise solution with potential applications extending beyond laparoscopic surgery to other minimally invasive procedures.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143624476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0