IEEE Transactions on Biomedical Engineering最新文献_第2页

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 : 2025-01-15 DOI: 10.1109/TBME.2024.3503459

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 : 2025-01-15 DOI: 10.1109/TBME.2024.3503457

引用次数: 0

2024 Index IEEE Transactions on Biomedical Engineering Vol. 71 2024索引IEEE生物医学工程学报第71卷

IF 4.4 2区医学

IEEE Transactions on Biomedical Engineering Pub Date : 2025-01-01 DOI: 10.1109/TBME.2024.3523752

引用次数: 0

Volumetric Ultrasound of the Plantar Soft Tissue Under Bodyweight Loading 体重负荷下足底软组织的体积超声研究

IF 4.4 2区医学

IEEE Transactions on Biomedical Engineering Pub Date : 2024-12-26 DOI: 10.1109/TBME.2024.3456001

Lynda M. Brady;William R. Ledoux

{"title":"Volumetric Ultrasound of the Plantar Soft Tissue Under Bodyweight Loading","authors":"Lynda M. Brady;William R. Ledoux","doi":"10.1109/TBME.2024.3456001","DOIUrl":"https://doi.org/10.1109/TBME.2024.3456001","url":null,"abstract":"<italic>Objective: This work aims to develop a device capable of acquiring volumetric scans of the plantar soft tissue in naturally loaded and unloaded states using ultrasound B-mode imaging and shear wave elastography. <italic>Methods: Materials were investigated for acoustic transmission and bodyweight loading. A mechanical scanning apparatus was constructed using a compatible load bearing material and two perpendicular linear actuators. Custom software was developed to control the scanner, record subject and scan information, and reconstruct acquired ultrasound images and shear wave speed values into a volume. The system was evaluated using custom-developed ultrasound phantoms. <italic>Results: Plastic materials reduced axial and lateral resolution by 0.25 - 0.5 mm and reduced SWE values by 0.8 to 26 kPa. The developed system produced volumetric scans within 0.1 to 1.6 mm of expected dimensions on a geometric phantom compared to 0 to 0.6 mm in standard computed tomography. Acoustic thermal increases were 0 °C for B-mode and 0.9 to 2.9 °C for SWE. Volumes of an anatomically realistic phantom and a pilot scan yielded clear anatomic features. <italic>Conclusion: The resulting system is capable of producing volumetric plantar soft tissue scans in both B-mode and shear wave elastography with resolution on par with existing volumetric medical imaging systems. <italic>Significance: This system images plantar soft tissue volumes under physiologic loads.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"72 2","pages":"493-502"},"PeriodicalIF":4.4,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993261","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 生物医学工程论文集作者须知

IF 4.4 2区医学

IEEE Transactions on Biomedical Engineering Pub Date : 2024-11-21 DOI: 10.1109/TBME.2024.3479173

引用次数: 0

IEEE Transactions on Biomedical Engineering Handling Editors Information 电气和电子工程师学会《生物医学工程论文集》处理编辑信息

IF 4.4 2区医学

IEEE Transactions on Biomedical Engineering Pub Date : 2024-11-21 DOI: 10.1109/TBME.2024.3479175

引用次数: 0

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

IF 4.4 2区医学

IEEE Transactions on Biomedical Engineering Pub Date : 2024-11-21 DOI: 10.1109/TBME.2024.3479171

引用次数: 0

Computationally Efficient SVD Filtering for Ultrasound Flow Imaging and Real-Time Application to Ultrafast Doppler 超声波流动成像的计算高效 SVD 滤波及超快多普勒的实时应用

IF 4.4 2区医学

IEEE Transactions on Biomedical Engineering Pub Date : 2024-11-12 DOI: 10.1109/TBME.2024.3479414

B. Pialot;F. Guidi;G. Bonciani;F. Varray;T. Loupas;P. Tortoli;A. Ramalli

{"title":"Computationally Efficient SVD Filtering for Ultrasound Flow Imaging and Real-Time Application to Ultrafast Doppler","authors":"B. Pialot;F. Guidi;G. Bonciani;F. Varray;T. Loupas;P. Tortoli;A. Ramalli","doi":"10.1109/TBME.2024.3479414","DOIUrl":"10.1109/TBME.2024.3479414","url":null,"abstract":"Over the past decade, ultrasound microvasculature imaging has seen the rise of highly sensitive techniques, such as ultrafast power Doppler (UPD) and ultrasound localization microscopy (ULM). The cornerstone of these techniques is the acquisition of a large number of frames based on unfocused wave transmission, enabling the use of singular value decomposition (SVD) as a powerful clutter filter to separate microvessels from surrounding tissue. Unfortunately, SVD is computationally expensive, hampering its use in real-time UPD imaging and weighing down the ULM processing chain, with evident impact in a clinical context. To solve this problem, we propose a new approach to implement SVD filtering, based on simplified and elementary operations that can be optimally parallelized on GPU (GPU sSVD), unlike standard SVD algorithms that are mainly serial. First, we show that GPU sSVD filters UPD and ULM data with high computational efficiency compared to standard SVD implementations, and without losing image quality. Second, we demonstrate that the proposed method is suitable for real-time operation. GPU sSVD was embedded in a research scanner, along with the spatial similarity matrix (SSM), a well-known efficient approach to automate the selection of SVD blood components. High real-time throughput of GPU sSVD is demonstrated when using large packets of frames, with and without SSM. For example, more than 15000 frames/s were filtered with 512 packet size on a 128 × 64 samples beamforming grid. Finally, GPU sSVD was used to perform, for the first time, UPD imaging with real-time and adaptive SVD filtering on healthy volunteers.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"72 3","pages":"921-929"},"PeriodicalIF":4.4,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10750461","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142619356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Novel Methodology for Intracranial Pressure Subpeak Identification Enabling Morphological Feature Analysis. 颅内压子峰值识别的新方法，支持形态特征分析。

IF 4.4 2区医学

IEEE Transactions on Biomedical Engineering Pub Date : 2024-11-11 DOI: 10.1109/TBME.2024.3495542

Varun Vinayak Kalaiarasan, Marcella Miller, Xu Han, Brandon Foreman, Xiaodong Jia

{"title":"A Novel Methodology for Intracranial Pressure Subpeak Identification Enabling Morphological Feature Analysis.","authors":"Varun Vinayak Kalaiarasan, Marcella Miller, Xu Han, Brandon Foreman, Xiaodong Jia","doi":"10.1109/TBME.2024.3495542","DOIUrl":"https://doi.org/10.1109/TBME.2024.3495542","url":null,"abstract":"Objective: The objective of this study is to propose a novel methodology for intracranial pressure (ICP) waveform subpeak identification by incorporating arterial blood pressure (ABP) and electrocardiogram (ECG) signals from patients who have undergone traumatic brain injury (TBI).Methods: This approach consisted of 1) multimodal signal pre-processing and initial manual ICP waveform morphology labeling, 2) semi-supervised training of a support vector machine (SVM) ICP waveform morphological classifier, and 3) a dynamic time warping barycenter averaging (DBA) based ICP waveform template generation and derivative dynamic time warping (DDTW)-driven ICP waveform subpeak mapping from template to incoming processed waveforms.Results: This proposed framework was evaluated on 30,000 ICP waveforms and resulted in an overall subpeak identification accuracy score of 98.2%.Conclusion: The results showcased an improvement over existing methodologies and showed resilience to variations in ICP waveform morphologies from patient to patient due to the incorporation of a subject matter expert (SME) to accommodate new and unseen ICP morphologies.Significance: The robustness of this comprehensive approach enabled the analysis of ICP morphological features over time to provide clinicians with crucial insights regarding the development of secondary pathologies in patients and facilitate monitoring their physiological state.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142619355","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

Personalized Blood Glucose Forecasting From Limited CGM Data Using Incrementally Retrained LSTM. 利用增量再训练 LSTM 从有限的 CGM 数据中进行个性化血糖预测

IF 4.4 2区医学

IEEE Transactions on Biomedical Engineering Pub Date : 2024-11-08 DOI: 10.1109/TBME.2024.3494732

Yiheng Shen, Samantha Kleinberg

{"title":"Personalized Blood Glucose Forecasting From Limited CGM Data Using Incrementally Retrained LSTM.","authors":"Yiheng Shen, Samantha Kleinberg","doi":"10.1109/TBME.2024.3494732","DOIUrl":"https://doi.org/10.1109/TBME.2024.3494732","url":null,"abstract":"For people with Type 1 diabetes (T1D), accurate blood glucose (BG) forecasting is crucial for the effective delivery of insulin by Artificial Pancreas (AP) systems. Deep learning frameworks like Long Short-Term-Memory (LSTM) have been widely used to predict BG using continuous glucose monitor (CGM) data. However, these methods usually require large amounts of training data for personalized forecasts. Moreover, individuals with diabetes exhibit diverse glucose variability (GV), resulting in varying forecast accuracy. To address these limitations, we propose a novel deep learning framework: Incrementally Retrained Stacked LSTM (IS-LSTM). This approach gradually adapts to individuals' data and employs parameter-transfer for efficiency. We compare our method to three benchmarks using two CGM datasets from individuals with T1D: OpenAPS and Replace-BG. On both datasets, our approach significantly reduces root mean square error compared to the state of the art (Stacked LSTM): from 14.55 to 10.23mg/dL (OpenAPS) and 17.15 to 13.41mg/dL (Replace-BG) at 30-minute Prediction Horizon (PH). Clarke error grid analysis demonstrates clinical feasibility with at least 98.81% and 97.25% of predictions within the clinically safe zone at 30- and 60-minute PHs. Further, we demonstrate the effectiveness of our method in cold-start scenarios, which helps new CGM users obtain accurate predictions.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142604073","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