IEEE Transactions on Biomedical Engineering最新文献_第5页

Is EMG Information Necessary for Deep Learning Estimation of Joint and Muscle Level States? 肌电图信息是深度学习估计关节和肌肉水平状态所必需的吗？

IF 4.4 2区医学

IEEE Transactions on Biomedical Engineering Pub Date : 2025-06-05 DOI: 10.1109/TBME.2025.3577084

Ethan B Schonhaut, Keaton L Scherpereel, Aaron J Young

{"title":"Is EMG Information Necessary for Deep Learning Estimation of Joint and Muscle Level States?","authors":"Ethan B Schonhaut, Keaton L Scherpereel, Aaron J Young","doi":"10.1109/TBME.2025.3577084","DOIUrl":"https://doi.org/10.1109/TBME.2025.3577084","url":null,"abstract":"Objective: Accurate, non-invasive methods for estimating joint and muscle physiological states have the potential to greatly enhance control of wearable devices during real-world ambulation. Traditional modeling approaches and current estimation methods used to predict muscle dynamics often rely on complex equipment or computationally intensive simulations and have difficulty estimating across a broad spectrum of tasks or subjects.Methods: Our approach used deep learning (DL) models trained on kinematic inputs to estimate internal physiological states at the knee, including moment, power, velocity, and force. We assessed each model's performance against ground truth labels from both a commonly used, standard OpenSim musculoskeletal model without EMG (static optimization) and an EMG-informed method (CEINMS), across 28 different cyclic and noncyclic tasks.Results: EMG provided no benefit for joint moment/power estimation (e.g., biological moment), but was critical for estimating muscle states. Models trained with EMG-informed labels but without EMG as an input to the DL system significantly outperformed models trained without EMG (e.g., 33.7% improvement for muscle moment estimation) (p < 0.05). Models that included EMG-informed labels and EMG as a model input demonstrated even higher performance (49.7% improvement for muscle moment estimation) (p < 0.05), but require the availability of EMG during model deployment, which may be impractical.Conclusion/significance: While EMG information is not necessary for estimating joint level states, there is a clear benefit during muscle level state estimation. Our results demonstrate excellent tracking of these states with EMG included only during training, highlighting the practicality of real-time deployment of this approach.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144233996","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

GONet: A Generalizable Deep Learning Model for Glaucoma Detection. GONet：青光眼检测的可推广深度学习模型。

IF 4.4 2区医学

IEEE Transactions on Biomedical Engineering Pub Date : 2025-06-04 DOI: 10.1109/TBME.2025.3576688

Or Abramovich, Hadas Pizem, Jonathan Fhima, Eran Berkowitz, Ben Gofrit, Meishar Meisel, Meital Baskin, Jan Van Eijgen, Ingeborg Stalmans, Eytan Z Blumenthal, Joachim A Behar

{"title":"GONet: A Generalizable Deep Learning Model for Glaucoma Detection.","authors":"Or Abramovich, Hadas Pizem, Jonathan Fhima, Eran Berkowitz, Ben Gofrit, Meishar Meisel, Meital Baskin, Jan Van Eijgen, Ingeborg Stalmans, Eytan Z Blumenthal, Joachim A Behar","doi":"10.1109/TBME.2025.3576688","DOIUrl":"https://doi.org/10.1109/TBME.2025.3576688","url":null,"abstract":"Glaucomatous optic neuropathy (GON), affecting an estimated 64.3 million people globally, causes irreversible vision loss when not detected early. Traditional diagnosis requires time-consuming ophthalmic examinations by specialists. Recent deep learning models for automating GON detection from colour fundus photographs (CFP) have shown promise but often suffer from limited generalizability across different ethnicities, disease groups and examination settings. To address these limitations, we introduce GONet, a robust deep learning model developed using seven independent datasets, including over 119,000 CFPs with gold-standard annotations and from patients of diverse geographic backgrounds. GONet consists of a DINOv2 pre-trained self-supervised vision transformers fine-tuned using a multisource domain strategy. GONet demonstrated high out-of-distribution generalizability, with an AUC of 0.88-0.99 in target domains. GONet performance was similar or superior to state-of-the-art works and the cup-to-disc ratio, by up to 18.4%. GONet is available at [URL provided on publication]. We also contribute a new dataset consisting of 747 CFPs with GON labels as open access, available at [URL provided on publication].","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144225350","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

Evaluating electrophysiological and behavioral measures of neural health in cochlear implant users: a computational simulation study. 评估人工耳蜗使用者神经健康的电生理和行为测量：一项计算模拟研究。

IF 4.4 2区医学

IEEE Transactions on Biomedical Engineering Pub Date : 2025-06-04 DOI: 10.1109/TBME.2025.3573398

Yixuan Zhang, Daniel Kipping, Waldo Nogueira

{"title":"Evaluating electrophysiological and behavioral measures of neural health in cochlear implant users: a computational simulation study.","authors":"Yixuan Zhang, Daniel Kipping, Waldo Nogueira","doi":"10.1109/TBME.2025.3573398","DOIUrl":"https://doi.org/10.1109/TBME.2025.3573398","url":null,"abstract":"Objective: Neural health refers to the condition and functionality of the auditory nerve fibers (ANFs), essential for transmitting sound signals from the cochlea to the brain. However, neural health cannot be directly measured due to current technological limitations. We utilize a computational model to evaluate different indirect methods for estimating neural health.Method: Two distinct measures for estimating neural health, (i) threshold levels for focused partial tripolar stimulation and (ii) changes in the electrically evoked compound action potential (eCAP) amplitude growth function for different inter-phase gaps (IPGs), were evaluated in a computational model of an electrically stimulated implanted cochlea. The model combined a 3D finite element method model, a realistic ANF geometry, and a neuron model, including an existing phenomenological single-ANF model and an eCAP model. Our experiments simulated different neural health conditions (healthy, shrunk, and degenerated) to model nueral dead region in the cochlea.Results: Experiment results demonstrated that the threshold levels with partial tripolar stimulation were more sensitive to neural health deficits than monopolar stimulation. The threshold difference between partial tripolar and monopolar stimulation seems to be a promising measure of neural health status. However, results from the eCAP IPG slope and offset effects were not consistently associated with neural health conditions.Conclusion: Our results suggest that the difference in threshold levels with partial tripolar and monopolar stimulation is a possible method for estimating neural health.Significance: This study enhances the understanding of neural health through a computational model, contributing to new approaches for neural health estimation.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144225338","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

Phase Correction of MR Spectroscopic Imaging Data Using Model-Based Signal Estimation and Extrapolation. 基于模型的信号估计和外推的MR光谱成像数据相位校正。

IF 4.4 2区医学

IEEE Transactions on Biomedical Engineering Pub Date : 2025-06-04 DOI: 10.1109/TBME.2025.3576330

Wen Jin, Rong Guo, Yudu Li, Yibo Zhao, Xin Li, Xiao-Hong Zhu, Wei Chen, Zhi-Pei Liang

{"title":"Phase Correction of MR Spectroscopic Imaging Data Using Model-Based Signal Estimation and Extrapolation.","authors":"Wen Jin, Rong Guo, Yudu Li, Yibo Zhao, Xin Li, Xiao-Hong Zhu, Wei Chen, Zhi-Pei Liang","doi":"10.1109/TBME.2025.3576330","DOIUrl":"https://doi.org/10.1109/TBME.2025.3576330","url":null,"abstract":"Objective: To develop an effective method for phase correction of magnetic resonance spectroscopic imaging (MRSI) data.Methods: In many MRSI applications, it is desirable to generate absorption-mode spectra, which requires correction of phase errors in the measured MRSI data. Conventional phase correction methods are sensitive to measurement noise and baseline distortion, often resulting in distorted absorption-mode spectra from MRSI data with low-SNR and long acquisition dead time. This paper proposed a novel model-based method for improved phase correction of MRSI data. The proposed method determined the zeroth-order phase and acquisition dead time using a Lorentzian-based spectral model and performed signal extrapolation using a generalized series model. Absorption-mode spectra were then generated from the phase-corrected and extrapolated MRSI data.Results: The proposed method was evaluated using both simulated data and experimental data acquired from human subjects in multi-nuclei (31P, 2H, and 1H) MRSI experiments. Simulation results demonstrated improved parameter estimation accuracy by the proposed method under various noise levels and dead times. The proposed method also consistently generated high-quality absorption-mode spectra with minimal spectral distortions from experimental data. The proposed method was compared with state-of-the-art methods (including the entropy method and LCModel method) and showed more robust phase correction performance with less spectral distortions.Conclusion: This paper introduced a novel method for phase correction of MRSI data. Results from simulated and in vivo data demonstrated that high-quality absorption-mode spectra could be obtained using the proposed method.Significance: This method will provide a useful tool for processing MRSI data.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144225351","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

A hybrid distributed capacitance birdcage coil for small-animal MR imaging at 14.1 T. 用于小动物14.1 T磁共振成像的混合式分布电容鸟笼线圈。

IF 4.4 2区医学

IEEE Transactions on Biomedical Engineering Pub Date : 2025-06-02 DOI: 10.1109/TBME.2025.3575398

Youheng Sun, Miutian Wang, Jinhao Liu, Yang Zhou, Wentao Wang, Hongwei Li, Weimin Wang, Qiushi Ren

{"title":"A hybrid distributed capacitance birdcage coil for small-animal MR imaging at 14.1 T.","authors":"Youheng Sun, Miutian Wang, Jinhao Liu, Yang Zhou, Wentao Wang, Hongwei Li, Weimin Wang, Qiushi Ren","doi":"10.1109/TBME.2025.3575398","DOIUrl":"https://doi.org/10.1109/TBME.2025.3575398","url":null,"abstract":"Objective: To develop a transceiver radio frequency (RF) coil optimized for high resolution small-animal imaging at 14.1 T, aimed at enhancing signal-to-noise ratio (SNR) performance.Methods: A hybrid distributed capacitance (HDC) birdcage coil was designed, combining conventional endring lumped capacitors with distributed capacitance along the legs, implemented using double-layer copper-clad substrates. Electromagnetic (EM) simulations were employed to optimize the coil's structural parameters and capacitance values for maximum RF performance. The HDC birdcage coil's performance was evaluated against a conventional bandpass (BP) design through electromagnetic simulations, bench tests, and phantom imaging. In vivo validation was performed using mouse imaging.Results: EM simulations demonstrated that the HDC design enhances mean $text{B}_{1}^{+}$ and $text{B}_{1}^{-}$ field strengths by 11.8% and 11.7%, respectively, relative to the conventional BP design. The HDC design also showed reduced electric field (E-field) value in phantom, with 4.2% lower mean and 11.4% lower maximum E-field value. Bench measurements revealed a superior quality factor (Q factor) for the HDC coil, with a 34.2% higher unloaded Q value compared to the conventional design. Phantom imaging confirmed a 41% SNR improvement with the HDC design. The optimized HDC coil enabled mouse brain imaging at 50 $mu$m resolution.Conclusion: The proposed HDC birdcage coil demonstrated superior receiver sensitivity and Q factor compared to conventional designs, yielding significant SNR improvements in 14.1 T imaging.Significance: The results demonstrated the feasibility of achieving enhanced coil performance through HDC design at ultra-high field strength, providing a promising approach for improving image quality in small-animal MRI applications.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144208425","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

A Birdcage Volume Transmit Coil and 8 Channel Receive Array for Marmoset Brain Imaging at 7T. 笼形体积发射线圈和8通道接收阵列用于7T狨猴脑成像。

IF 4.4 2区医学

IEEE Transactions on Biomedical Engineering Pub Date : 2025-06-02 DOI: 10.1109/TBME.2025.3576064

Pedram Yazdanbakhsh, Maeva Gacoin, Marcus J Couch, Tyler Cook, Ilana R Leppert, David A Rudko, Justine Clery

{"title":"A Birdcage Volume Transmit Coil and 8 Channel Receive Array for Marmoset Brain Imaging at 7T.","authors":"Pedram Yazdanbakhsh, Maeva Gacoin, Marcus J Couch, Tyler Cook, Ilana R Leppert, David A Rudko, Justine Clery","doi":"10.1109/TBME.2025.3576064","DOIUrl":"https://doi.org/10.1109/TBME.2025.3576064","url":null,"abstract":"Objective: To design and fabricate a band-pass birdcage volume resonator and eight channel, conformal receive array coil for MRI of both awake and anesthetized marmoset brain at 7T. The coil is compatible with a whole body 7T clinical MRI scanner running in single channel transmit (sTx) mode.Methods: The marmoset head coil included a shielded, band-pass birdcage transmit coil with 24 legs, as well as 8 overlapped receive elements. Electromagnetic (EM) field simulation was performed for the 24 leg band pass birdcage Tx coil to calculate the B1 + efficiency. The efficacy of both transmit and receive coil designs were evaluated by measuring standard coil performance metrics. This was done while imaging a marmoset head phantom, as well as by acquiring in vivo, anesthetized and awake marmoset images.Results: The transmit coil along with the optimized receive array produced high resolution (0.8 mm isotropic for EPI images; 0.36 mm isotropic for structural images) and high SNR (between 50 and 80) images of the marmoset brain. The simulated B1 + efficiency of the birdcage at the center of the phantom was 2.6 μT/sqrt (W).Conclusion and significance: A shielded, band-pass birdcage transmit coil was designed and fabricated for marmoset brain imaging at 7T. An 8-channel receive array consisting of eight overlapped loops, covering the whole brain of the marmoset, was also constructed and applied for signal reception. The system successfully allowed scanning of both young and older marmosets. It is well-suited for longitudinal studies of marmoset brain structure. The coil advantageously allows the study of neurodevelopment and primate brain function.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144208424","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

Phantom-Based Ultrasound-ECG Deep Learning Framework for Prospective Cardiac Computed Tomography. 基于图像的超声-心电深度学习框架的前瞻性心脏计算机断层扫描。

IF 4.4 2区医学

IEEE Transactions on Biomedical Engineering Pub Date : 2025-05-30 DOI: 10.1109/TBME.2025.3575268

Shambavi Ganesh, Brooks D Lindsey, Srini Tridandapani, Pamela T Bhatti

{"title":"Phantom-Based Ultrasound-ECG Deep Learning Framework for Prospective Cardiac Computed Tomography.","authors":"Shambavi Ganesh, Brooks D Lindsey, Srini Tridandapani, Pamela T Bhatti","doi":"10.1109/TBME.2025.3575268","DOIUrl":"https://doi.org/10.1109/TBME.2025.3575268","url":null,"abstract":"Objective: We present the first multimodal deep learning framework combining ultrasound (US) and electrocardiography (ECG) data to predict cardiac quiescent periods (QPs) for optimized computed tomography angiography gating (CTA).Methods: The framework integrates a 3D convolutional neural network (CNN) for US data and an artificial neural network (ANN) for ECG data. A dynamic heart motion phantom, replicating diverse cardiac conditions, including arrhythmias, was used to validate the framework. Performance was assessed across varying QP lengths, cardiac segments, and motions to simulate real-world conditions.Results: The multimodal US-ECG 3D CNN-ANN framework demonstrated improved QP prediction accuracy compared to single-modality ECG-only gating, achieving 96.87% accuracy compared to 85.56%, including scenarios involving arrhythmic conditions. Notably, the framework shows higher accuracy for longer QP durations (100 ms - 200 ms) compared to shorter durations (<100ms), while still outperforming single-modality methods, which often fail to detect shorter quiescent phases, especially in arrhythmic cases. Consistently outperforming single-modality approaches, it achieves reliable QP prediction across cardiac regions, including the whole phantom, interventricular septum, and cardiac wall regions. Analysis of QP prediction accuracy across cardiac segments demonstrated an average accuracy of 92% in clinically relevant echocardiographic views, highlighting the framework's robustness.Conclusion: Combining US and ECG data using a multimodal framework improves QP prediction accuracy under variable cardiac motion, particularly in arrhythmic conditions.Significance: Since even small errors in cardiac CTA can result in non-diagnostic scans, the potential benefits of multimodal gating may improve diagnostic scan rates in patients with high and variable heart rates and arrhythmias.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144186886","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

Integrating Motor Unit Activity With Deep Learning for Real-Time, Simultaneous and Proportional Wrist Angle and Grasp Force Estimation. 将运动单元活动与深度学习相结合，用于实时、同步和比例的手腕角度和握力估计。

IF 4.4 2区医学

IEEE Transactions on Biomedical Engineering Pub Date : 2025-05-30 DOI: 10.1109/TBME.2025.3575252

Dongxuan Li, Chen Chen, Kezhe Zhu, Ruye Guo, Peter B Shull

{"title":"Integrating Motor Unit Activity With Deep Learning for Real-Time, Simultaneous and Proportional Wrist Angle and Grasp Force Estimation.","authors":"Dongxuan Li, Chen Chen, Kezhe Zhu, Ruye Guo, Peter B Shull","doi":"10.1109/TBME.2025.3575252","DOIUrl":"https://doi.org/10.1109/TBME.2025.3575252","url":null,"abstract":"Objective: Myoelectric prostheses offer great promise in enabling amputees to perform daily activities independently. However, existing neural interfaces generally cannot simultaneously and proportionally decode kinematics and kinetics in real time, nor can they directly interpret neural commands. We thus propose a novel framework that integrates motor unit activity with deep learning and demonstrate its efficiency in the real-time, simultaneous, and proportional estimation of wrist angles and grasp forces.Methods: This framework utilizes real-time high-density surface electromyography decomposition to identify motor neuron discharges, followed by neural drive computation integrated with a modular Long Short-Term Memory-based neural network. Ten subjects participated in the experiments involving wrist pronation/supination, flexion/extension, and abduction/adduction, with varying grasp force.Results: The proposed framework significantly outperformed five baseline methods, achieving an nRMSE of 13.6% and 11.1% and an R2 of 73.2% and 76.8% for wrist angle and grasp force, respectively. In addition, we further characterized the spatial distribution and recruitment patterns of motor units during movement generation.Conclusion: These findings highlight the feasibility of integrating neural drive insights with deep learning methods to improve simultaneous and proportional estimation performance.Significance: The proposed framework has the potential to enhance the independence and quality of life of prosthetic users by enabling them to perform a wider range of tasks with improved precision and control over both kinematics and kinetics.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144186885","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

Hierarchical Transformer Fusion of Gaze Attention and Muscle Activity for Forearm Movement Estimation. 前臂运动估计中注视注意与肌肉活动的层次变压器融合。

IF 4.4 2区医学

IEEE Transactions on Biomedical Engineering Pub Date : 2025-05-29 DOI: 10.1109/TBME.2025.3575202

Bangyu Lan, Stefano Stramigioli, Kenan Niu

{"title":"Hierarchical Transformer Fusion of Gaze Attention and Muscle Activity for Forearm Movement Estimation.","authors":"Bangyu Lan, Stefano Stramigioli, Kenan Niu","doi":"10.1109/TBME.2025.3575202","DOIUrl":"https://doi.org/10.1109/TBME.2025.3575202","url":null,"abstract":"Tracking forearm movement via measured physiological signals is crucial for understanding human motor control mechanism. Current methods mainly use muscle-derived signals to predict arm movements while often overlooking the potential role of gaze attention, which is important for hand-eye coordination and instant and continuous motion planning and execution. In this study, we explored the impact of gaze on motion tracking. A hierarchical transformer-based structure was developed to integrate gaze into muscle activity signals for recovering the joint trajectory. To collect the dataset, six subjects were recruited to perform arm motions broadly involved in daily activities; the measured signals from the muscle activity and gaze attention were used to train and evaluate the proposed method. A performance comparison was conducted between the models using solely muscle activity signals and both muscle and gaze information. The experimental results showed the important role of gaze information involved in motion prediction and the motor control mechanism. This research also gained insights on how to integrate gaze information into the muscle signals, which offers an alternative to bringing artificial intelligence to be engaged in the framework of motion tracking. Consequently, it is important for future designs of biomechanical sensors and wearable robotics systems.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144180361","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

Deep Separable Spatiotemporal Learning for Fast Dynamic Cardiac MRI. 快速动态心脏MRI的深度可分离时空学习。

IF 4.4 2区医学

IEEE Transactions on Biomedical Engineering Pub Date : 2025-05-28 DOI: 10.1109/TBME.2025.3574090

Zi Wang, Min Xiao, Yirong Zhou, Chengyan Wang, Naiming Wu, Yi Li, Yiwen Gong, Shufu Chang, Yinyin Chen, Liuhong Zhu, Jianjun Zhou, Congbo Cai, He Wang, Xianwang Jiang, Di Guo, Guang Yang, Xiaobo Qu

{"title":"Deep Separable Spatiotemporal Learning for Fast Dynamic Cardiac MRI.","authors":"Zi Wang, Min Xiao, Yirong Zhou, Chengyan Wang, Naiming Wu, Yi Li, Yiwen Gong, Shufu Chang, Yinyin Chen, Liuhong Zhu, Jianjun Zhou, Congbo Cai, He Wang, Xianwang Jiang, Di Guo, Guang Yang, Xiaobo Qu","doi":"10.1109/TBME.2025.3574090","DOIUrl":"https://doi.org/10.1109/TBME.2025.3574090","url":null,"abstract":"Objective: Dynamic magnetic resonance imaging (MRI) plays an indispensable role in cardiac diagnosis. To enable fast imaging, the k-space data can be undersampled but the image reconstruction poses a great challenge of high-dimensional processing. This challenge necessitates extensive training data in deep learning reconstruction methods. In this work, we propose a novel and efficient approach, leveraging a dimension-reduced separable learning scheme that can perform exceptionally well even with highly limited training data.Methods: We design this new approach by incorporating spatiotemporal priors into the development of a Deep Separable Spatiotemporal Learning network (DeepSSL), which unrolls an iteration process of a 2D spatiotemporal reconstruction model with both temporal lowrankness and spatial sparsity. Intermediate outputs can also be visualized to provide insights into the network behavior and enhance interpretability.Results: Extensive results on cardiac cine datasets demonstrate that the proposed DeepSSL surpasses stateof-the-art methods both visually and quantitatively, while reducing the demand for training cases by up to 75%. Additionally, its preliminary adaptability to unseen cardiac patients has been verified through a blind reader study conducted by experienced radiologists and cardiologists. Furthermore, DeepSSL enhances the accuracy of the downstream task of cardiac segmentation and exhibits robustness in prospectively undersampled real-time cardiac MRI.Conclusion: DeepSSL is efficient under highly limited training data and adaptive to patients and prospective undersampling.Significance: This approach holds promise in addressing the escalating demand for high-dimensional data reconstruction in MRI applications.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144173606","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