IEEE Transactions on Biomedical Engineering最新文献

Objective: Harmonic Motion Imaging (HMI) is an ultrasound elasticity imaging method that measures the mechanical properties of tissue using amplitude-modulated acoustic radiation force (AM-ARF). Multi-frequency HMI (MF-HMI) excites tissue at various AM frequencies simultaneously, allowing for image optimization without prior knowledge of inclusion size and stiffness. However, challenges remain in size estimation as inconsistent boundary effects result in different perceived sizes across AM frequencies. Herein, we developed an automated assessment method for tumor and focused ultrasound surgery (FUS) induced lesions using a transformer-based multi-modality neural network, HMINet, and further automated neoadjuvant chemotherapy (NACT) response prediction. HMINet was trained on 380 pairs of MF-HMI and B-mode images of phantoms and in vivo orthotopic breast cancer mice (4T1). Test datasets included phantoms (n = 32), in vivo 4T1 mice (n = 24), breast cancer patients (n = 20), FUS-induced lesions in ex vivo animal tissue and in vivo clinical settings with real-time inference, with average segmentation accuracy (Dice) of 0.91, 0.83, 0.80, and 0.81, respectively. HMINet outperformed state-of-the-art models; we also demonstrated the enhanced robustness of the multi-modality strategy over B-mode-only, both quantitatively through Dice scores and in terms of interpretation using saliency analysis. The contribution of AM frequency based on the number of salient pixels showed that the most significant AM frequencies are 800 and 200 Hz across clinical cases.

Significance: We developed an automated, multimodality ultrasound-based tumor and FUS lesion assessment method, which facilitates the clinical translation of stiffness-based breast cancer treatment response prediction and real-time image-guided FUS therapy.

Objective: Real-time estimation of brain state is essential for efficient brain stimulation. Specifically, the electroencephalography (EEG) oscillation phase arose as a promising biomarker for instantaneous brain excitability, making it ideal for state-dependent brain stimulation. Current methods for real-time EEG phase extraction lose accuracy in the presence of non-stationary noise, motivating the development of a more robust and accurate algorithm. Here, we propose and validate Bayesian Temporal Prediction (BTP) as an effective method for EEG phase detection in real-time.

Methods: BTP utilizes a short pre-session EEG recording and learning of the personalized prediction parameters, enabling subsequent high-precision real-time phase detection. We experimentally validate BTP in humans and compare its performance to a strong benchmark algorithm.

Results: BTP demonstrates accurate EEG oscillation phase detection across a broad range of conditions and target oscillations, facilitating personalized brain stimulation.

Conclusion: This study introduces BTP as a robust, computationally efficient, and accurate method for EEG state-dependent stimulation.

Significance: The widespread adoption of BTP in research and clinical settings has the potential to enhance treatment efficacy and minimize inter- and intra-individual variability in brain stimulation interventions.

Objective: Data augmentation is important for enhancing subject-independent classification in deep learning (DL) approaches for steady-state visual evoked potential (SSVEP) brain-computer interfaces (BCIs) using electroencephalography (EEG). However, current augmentation techniques often inadequately exploit individual-specific style characteristics, limiting the model's robustness against inter-subject style variability. To tackle this problem, this study proposes a novel data augmentation method called Simultaneous Spatial-Energy Representation (SSER).

Methods: SSER employs singular value decomposition (SVD) to extract spatial and energy representations from EEG signals, effectively capturing style characteristics. These representations are independently mixed across source domains during signal reconstruction, generating novel domains that cover a broader range of styles. This strategy promotes the learning of domain-invariant features and enhances the model's robustness to style variability.

Results: Comprehensive experiments on public datasets demonstrate that SSER outperforms state-of-the-art data augmentation techniques and generalizes well across various DL models. Furthermore, self-collected offline and online experiments involving 30 subjects provide additional evidence of the method's effectiveness.

Conclusion: By simultaneously manipulating spatial and energy representations, SSER offers a richer characterization of EEG signal style variability, leading to superior performance.

Significance: The proposed innovative data augmentation method advances subject-independent classification, facilitating the broader application of EEG-based BCIs in real-world scenarios.

Objective: Stress has emerged as a major adverse factor affecting both physical and mental health. Timely recognition of stress is essential for effective stress management. However, substantial inter-individual variability in stress responses often leads to distribution shifts, thereby making conventional generic stress recognition models ineffective in adapting to such changes.

Methods: To address distribution shifts caused by inter-individual variability, this paper proposes the Variational Instance-Adaptive Stress recognition (VIAStress), a Domain Generalization (DG)-based framework that consists of two main components: Variational Instance Adaptation (VIA) and Multimodal Domain Generalization (MMDG). The VIA leverages the distribution-fitting capability of variational inference to model classifier parameters as instance-conditioned distributions, dynamically adjusting decision boundaries for personalized stress recognition. In addition, the model's feature extractor integrates the MMDG strategy to facilitate information interaction between PPG and EDA, thereby enhancing the generalization of multimodal feature representations.

Results: VIAStress is evaluated on four public datasets, WESAD, UBFC-Phys, VerBIO, and CAN-STRESS. Under both within-dataset and cross-dataset subject-independent settings, the proposed method achieves superior generalization performance on unseen subjects in most tasks, outperforming competitive approaches.

Conclusion and significance: This study introduces VIAStress, a personalized stress recognition model designed to address the practical challenges of inter-individual variability. The results show that VIAStress exhibits strong adaptability and robustness to user differences, supporting the development of more effective and personalized stress management solutions.

Background: Bladder cancer (BC) symptoms often overlap with benign conditions, while no routine screening exists for general population. We aim to develop a machine learning (ML)-based screening pipeline for early BC detection using electronic-health-records (EHRs) in primary care.

Methods: A multi-centred case-control cohort (1995-2018; n = 64,884) was created for model training and testing. We further validated the model prospectively on an independent cohort (2019-2020; n = 4,569). We proposed the Parsimony driven REweighting for Calibrated Input-based Screening for Early detection-Adjustable Grey Zone (PRECISE-AGZ), which identified influential features from 48,261 candidates and developed a calibrated logistic regression screening model with optimised grey-zone thresholds.

Results: We finally identified 38 features, achieving an AUC (area under the curve) of 0.789 (95% CI: 0.780-0.798) on testing set. Neurological disorders (e.g., Parkinson's disease, OR: 0.86, 95% CI: 0.79-0.92) and medications (e.g., Tamoxifen, OR: 1.13, 95% CI: 1.07-1.20) emerged as novel predictors for BC screening. The screening model stratified the population into three risk categories based on predicted probability: low-risk (0.55), achieving a sensitivity of 0.852, F1-score of 0.799, and screening population coverage (SPC) of 34.5%. Applied to the prospective validation cohort, model performance varied by months before BC diagnosis, with sensitivities ranging from 0.872 (F1-score: 0.714, SPC: 29.9%) at the first month to 0.667 (F1-score: 0.690, SPC: 12.7%) at the twelfth month.

Conclusion: The PRECISE-AGZ pipeline efficiently identified clinical signals from EHRs for early BC detection, offering promising potential for implementing population-based BC screening.

Objective: Deep brain stimulation (DBS) is increasingly used in treating motor disorders, neurodegenerative conditions, and mental health conditions. However, treatments are limited to the most severe cases due to the invasiveness of the surgical procedure. We propose stimulating ventral brain areas using electrodes inserted through the nose and placed in contact with the inferior skull base bones, under the olfactory cleft and in the sphenoid sinus.

Methods: We designed a compact, low-power stimulation implant that fits in the sphenoid sinus and tested the implant insertion and operation in cadaveric heads, measuring the electric field obtained in deep brain regions. Using commercial multielectrode probes and an external stimulator, we also measured the electric field in cadaveric brains generated by different electrode placements to characterize the focus and steering that can be achieved.

Results: The implant can generate electric fields in the brain above 10 V/m, likely sufficient for neuronal activation. It is powered using either a 3 V battery which can generate almost 100 000 pulses from a 1 mAh charge, or alternatively, using inductive wireless power transfer, with the primary coil placed around the circumference of the head.

Conclusion: This first-of-its-kind sphenoid implant demonstrates the possibility of minimally-invasive, focused deep brain stimulation.

Significance: By enabling stimulation of deep brain regions without requiring surgery, transnasal stimulation can drastically improve the accessibility of some DBS treatments and broaden its applicability to additional mental health conditions.

Objective: To reduce variability in chest radiography (CXR) from acquisition and post-processing, and assess whether harmonization improves image quality and down-stream diagnostic performance.

Methods: A generative adversarial network (GAN) was trained exclusively on virtual images produced by a Virtual Imaging Trial (VIT). The model maps non-harmonized CXRs to noise-free, unprocessed references using a dual U-Net with contrastive loss. Evaluation spanned virtual, physical-phantom, and clinical data using generic (GIQMs) and chest-specific (CIQMs) metrics. A phantom benchmark compared the method with ComBat harmonization and a conventional denoising algorithm. Clinical generalization was tested on VinDr-CXRs, examining feature compactness via Uniform Manifold Approximation and Projection (UMAP). We also assessed NIH-CXRs, quantifying diagnostic accuracy with bootstrap uncertainty.

Results: Compared with non-harmonized images, harmonized CXRs yielded approximately 89% lower NRMSE, 50% higher PSNR, and 86% higher SSIM. On phantom data, CIQM variability fell from 0.255 to 0.026 and was reduced more consistently than with ComBat or denoising. Clinical analyses on VinDr-CXRs showed tighter UMAP clusters for harmonized features, indicating suppression of acquisition-related variability. On NIH-CXRs, training and testing a classifier in the harmonized domain improved diagnostic accuracy over the non-harmonized domain and lowered cross-domain sensitivity; pleural and cardiac categories showed consistent gains, while texture-dependent labels exhibited task-dependent effects.

Conclusion: VIT-guided training enables a GAN to harmonize CXRs toward a raw reference, improving quantification variability and harmonizing image representations across systems.

Significance: The proposed virtual-to-clinical strategy is scalable and generalizable, offering a practical path to standardized CXR appearance and reliable downstream detection across institutions.

Objective: Von Willebrand Disease (VWD), the most common inherited bleeding disorder affecting 0.1% to 1% of the population, causes extensive mucocutaneous bleeding across various clinical contexts. Von Willebrand Factor (vWF) plays a critical role in hemostasis by mediating platelet adhesion under high shear stress conditions. We simulated platelet-vWF interactions to investigate adhesion dynamics in VWD using a multiscale modeling approach combining Dissipative Particle Dynamics (DPD) and Coarse-Grained Molecular Dynamics (CGMD).

Methods: Our platelet model provides high-resolution insights into adhesion mechanics by representing the platelet as a complex, deformable cellular entity comprising intricate membrane and subcellular components that capture the nuanced biomechanical behavior of platelets under flow conditions.

Results: Simulations under 30 dyne/cm2 shear stress revealed a threshold effect: platelets failed to complete flipping and adhesion below 40% vWF density, mirroring Type 1 VWD clinical manifestations. We identified asymmetric platelet flipping dynamics with longer lift-off periods compared to reattachment periods, and revealed a distinct temporal lag between the platelet's vertical positioning and minimum bond force/contact area configurations. In vitro experiments supported these computational findings, demonstrating a significant reduction in platelet residence duration and translocation distance as vWF surface densities decreased.

Conclusions: This work provides quantitative insights into the molecular mechanisms underlying platelet adhesion in VWD through our advanced CGMD model.

Significance: Our findings establish a comprehensive framework for understanding cellular adhesion processes in biofluid environments, potentially informing therapeutic strategies for bleeding disorders and thrombotic conditions.

Objective: Emerging technologies for electromagnetic interference (EMI) elimination have enabled radio frequency (RF) shielding-free magnetic resonance imaging (MRI), significantly reduced costs and increased accessibility. Existing methods often rely on multiple external sensors for EMI elimination, which can degrade with fewer sensors. Our goal is to develop a method that robustly eliminates EMI with fewer or no sensors.

Methods: We propose a method for multi-channel electromagnetic interference elimination in shielding-free MRI using null operations (MEENO). This approach fully exploits the inter-channel correlation across all RF receiving and EMI sensing channels. The method was comprehensively evaluated through simulation studies and human brain imaging.

Results: The MEENO approach effectively eliminates EMI artifacts, outperforming existing methods, particularly with a limited number of sensors. It shows superior performance in terms of signal-to-noise ratio and residual EMI levels.

Conclusion: We introduce a method for EMI elimination in multi-channel MRI using null operations, which fully leverages inter-channel correlation and surpasses existing approaches, especially with limited sensors.

Significance: This work offers a solution for EMI elimination with fewer or no external sensors, providing a more cost-effective and robust approach for shielding-free MRI.

Objective: Electrocardiograms (ECGs) play a crucial role in diagnosing heart conditions; however, the effectiveness of artificial intelligence (AI)-based ECG analysis is often hindered by the limited availability of labeled data. Self-supervised learning (SSL) can address this by leveraging large-scale unlabeled data. We introduce PhysioCLR (Physiology-aware Contrastive Learning Representation for ECG), a physiology-aware contrastive learning framework that incorporates domain-specific priors to enhance the generalizability and clinical relevance of ECG-based arrhythmia classification. Methods: During pretraining, PhysioCLR learns to bring together embeddings of samples that share similar clinically relevant features while pushing apart those that are dissimilar. Unlike existing methods, our method integrates ECG physiological similarity cues into contrastive learning, promoting the learning of clinically meaningful representations. Additionally, we introduce ECG-specific augmentations that preserve the ECG category post-augmentation and propose a hybrid loss function to further refine the quality of learned representations. Results: We evaluate PhysioCLR on two public ECG datasets, Chapman and Georgia, for multilabel ECG diagnoses, as well as a private ICU dataset labeled for binary classification. Across the Chapman, Georgia, and private cohorts, PhysioCLR boosts the mean AUROC by 12% relative to the strongest baseline, underscoring its robust cross-dataset generalization. Conclusion: By embedding physiological knowledge into contrastive learning, PhysioCLR enables the model to learn clinically meaningful and transferable ECG features.  Significance: PhysioCLR demonstrates the potential of physiology-informed SSL to offer a promising path toward more effective and label-efficient ECG diagnostics.