Biomedical Signal Processing and Control最新文献

{"title":"VesselMamba: 3D vessel segmentation in CTA images using Mamba with enhanced Spatial-Channel Attention","authors":"Ziyue Xie ,&nbsp;Xiaoquan Huang ,&nbsp;Shiyao Chen ,&nbsp;Yonghong Shi","doi":"10.1016/j.bspc.2025.107982","DOIUrl":"10.1016/j.bspc.2025.107982","url":null,"abstract":"<div><div>3D vessel segmentation in Computed Tomography Angiography (CTA) is crucial yet challenging due to the complex, multi-scale, and elongated branching structure of human vasculature. Accurate modeling requires capturing both long-range dependencies and multi-scale information inherent in vascular networks. While deep neural networks like CNNs and Vision Transformers (ViTs) have demonstrated progress, they often face challenges balancing global receptive field capture with computational efficiency. To address these limitations, we propose VesselMamba, a novel 3D vessel segmentation framework based on Mamba, an approach for modeling long-range dependencies with linear complexity. VesselMamba integrates parallel Mamba blocks in the encoder to efficiently capture vascular continuity and long-range dependencies. Additionally, the encoder is enhanced with a Spatial-Channel Attention with Spatial Pyramid Pooling (SCASPP) module to effectively model multi-scale information and optimize the integration of global and local features, significantly improving segmentation precision. Furthermore, a composite loss function that combines the clDice loss with traditional cross-entropy and Dice losses is employed to improve the connectivity of segmented vessels. This reduces fragmentation and artifacts, leading to more reliable segmentations. Comprehensive ablation studies on private and public datasets demonstrate the complementary nature and effectiveness of the proposed modules. Experimental results show that VesselMamba achieves state-of-the-art performance in CTA vessel segmentation tasks, outperforming existing methods and providing a robust tool for clinical diagnosis and research.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"110 ","pages":"Article 107982"},"PeriodicalIF":4.9,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144147244","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}

{"title":"Kalman filter in quality assurance in radiotherapy: A practical application for daily dose quality control","authors":"Dimitri Reynard ,&nbsp;Jean-Baptiste Billet ,&nbsp;Alain Barraud ,&nbsp;Christophe Mazzara","doi":"10.1016/j.bspc.2025.108143","DOIUrl":"10.1016/j.bspc.2025.108143","url":null,"abstract":"<div><div>This study introduces a Kalman filter (KF)-based approach to enhance the daily dose quality control of radiotherapy equipment. The radiation production system is modeled as a dynamic system governed by state equations. The KF is applied to 30 months of daily dose quality control (DDQC) data from Varian® Halcyon systems delivering 6 MV flattening filter-free beams. Input measurements for the KF derive from quality control data collected with the Sun Nuclear® DailyQA 3 detector. Monitor units, rather than time, serve as the independent variable, with two iteration frequencies evaluated. The evolution model includes terms for monitor chamber aging and sensitivity corrections based on atmospheric pressure, with a second model further accounting for room temperature.</div><div>The KF extracts additional insights from the quality control measurements. Outliers are detected using a two-standard-deviation window, and drift prediction enables proactive scheduling of dose recalibrations. A significant correlation is observed between KF outputs and machine interventions, such as maintenance, recalibration, and component replacement.</div><div>Further refinements in the evolution model and the inclusion of additional input measurements could improve precision. The systematic collection and automated analysis of machine event logs could also enhance early issue detection and provide a more robust framework for decision-making. The lightweight and computationally efficient nature of KF models, combined with their scalability, suggests they could become a valuable tool for establishing a proactive, data-driven paradigm in radiotherapy quality assurance.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"110 ","pages":"Article 108143"},"PeriodicalIF":4.9,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144147242","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}

{"title":"Physiologic control of rotary blood pumps by ventricular chamber size estimation using resonantly coupled sensors","authors":"Yu Wang ,&nbsp;George A.F. Ghali ,&nbsp;Xiaoyue Luo ,&nbsp;Pramod Bonde ,&nbsp;Guruprasad A. Giridharan","doi":"10.1016/j.bspc.2025.108135","DOIUrl":"10.1016/j.bspc.2025.108135","url":null,"abstract":"<div><div>Rotary blood pumps (RBP) currently operate at a fixed pump speed and are unable to meet physiologic demand and susceptible to ventricular suction. To overcome this limitation, we developed a left ventricular end-diastolic volume (EDV) based physiologic control algorithm using resonantly coupled high-efficiency sensors. The resonantly coupled sensors consist of apical and outflow sensors that can accurately assess the ventricular chamber size with minimal long-term drift (∼1 %) at 9 months. The ability of the control algorithm was evaluated using an in-silico circulatory system model coupled to an axial or centrifugal flow RBP with 15 % uniformly distributed measurement noise. The EDV setpoint was set to 85 ml, and the efficacy of the EDV control algorithm was evaluated and compared to maintaining a constant pump speed during (1) rest and exercise; (2) rapid, eight-fold augmentation of pulmonary vascular resistance; and (3) rapid transitions between rest and exercise. Safety and robustness of the algorithm was also evaluated by assuming a 6 % volume drift. The EDV control algorithm provided sufficient physiological perfusion and avoided ventricular suction in all cases. Performance of the EDV algorithm was superior compared to maintaining constant pump speed for both types of RBP, demonstrating pump independence of the proposed algorithm.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"110 ","pages":"Article 108135"},"PeriodicalIF":4.9,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144147455","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}

{"title":"Precision applied behavior analysis intervention for autism spectrum disorder using natural language processing and graph centrality","authors":"Manu Kohli ,&nbsp;Monica Juneja ,&nbsp;Manushree Gupta ,&nbsp;Arpan Kumar Kar ,&nbsp;Smitha Sairam ,&nbsp;Varun Ganjigunte Prakash ,&nbsp;Prathosh A.P.","doi":"10.1016/j.bspc.2025.108034","DOIUrl":"10.1016/j.bspc.2025.108034","url":null,"abstract":"<div><div>The increased prevalence of Autism Spectrum Disorder (ASD) and the urgent need for personalized treatment have highlighted the role of data science in enhancing clinicians’ capacity and treatment quality. Application of Natural Language Processing (NLP) has created new paradigms by analyzing and finding similarities between the treatment prescriptions extracted from Electronic Health Records (EHRs). Social Network Analysis (SNA) and centrality computation methods have opened new avenues to identify behavior patterns and mental health symptoms, forecasting therapy progression and personalization trajectories. In this paper, we develop a novel SNA graph model by preprocessing longitudinal Applied Behavior Analysis (ABA) treatment data of 29 patients using NLP methods and computing various centrality scores. We perform community detection at various temporal points during the six-month intervention duration and find patient similarity based on prescription and socio-demographic similarity-building edge weights. We develop a treatment recommendation model and match its outcome on recommendation and effectiveness measures with the ground truth. Our contribution explores novel approaches in determining the node influence of centrality measures on patient-level skill acquisition and treatment recommendation.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"110 ","pages":"Article 108034"},"PeriodicalIF":4.9,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144147456","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}

{"title":"A multi-stage progressive multi-attention transformer for optical magnification medical images super-resolution","authors":"Mingxuan Li ,&nbsp;Jinbao Li ,&nbsp;Yingchun Cui","doi":"10.1016/j.bspc.2025.108066","DOIUrl":"10.1016/j.bspc.2025.108066","url":null,"abstract":"<div><div>Optical magnification medical images are essential for doctors diagnosing diseases by analyzing patterns, textures, and regional structures. However, optical magnification medical images are often affected by various factors such as device limitations, changes in lighting, and the condition of the patient’s lesion area, resulting in low resolution and partial blurring of the obtained image results, which in turn affects subsequent diagnosis and treatment. Although super-resolution is an effective way to improve the clarity of medical images, most super-resolution models still face a series of challenges when applied to optical magnification medical images: on the one hand, most models are single-stage architecture design that cannot fully utilize multi-scale information, and on the other hand, whether the state-of-the-art methods can maintain superior performance and applicability on different medical image datasets. To address these challenges, we propose a Multi-Stage Progressive Multi-Attention Transformer (MPMAT) framework, which captures pixel information of small-, medium-, and large-scale images through a three-stage design and utilizes feature information of different scales to generate higher-quality medical images. Then, we propose a Multi-Attention Group (MAG) method, combining various attention mechanisms with a Multi-Convolution Feature Fusion Block (MCFFB). This approach can enhances the integration of local and global features and addresses the spatial and channel feature extraction strategy problem. Finally, the experimental results demonstrate that MPMAT outperforms the state-of-the-art methods by a significant margin, achieving an improvement of 0.3 dB to 1.2 dB on multiple optical magnification image datasets.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"110 ","pages":"Article 108066"},"PeriodicalIF":4.9,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144147243","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}

{"title":"Efficient 3D magnetic resonance image reconstruction by 2D transformers and attention-based fusion model","authors":"Tianyi Yang ,&nbsp;Xiaohan Liu ,&nbsp;Yiming Liu ,&nbsp;Xuebin Sun ,&nbsp;Zhenchang Wang ,&nbsp;Yanwei Pang","doi":"10.1016/j.bspc.2025.108071","DOIUrl":"10.1016/j.bspc.2025.108071","url":null,"abstract":"<div><div>Utilizing 3D networks for reconstructing images from undersampled 3D <em>k</em>-space data shows potential in accelerating 3D MR imaging. CNN-based reconstruction network U-Net is such a classical method and is widely used in accelerating MRI research. However, directly reconstructing 3D MR images using 3D U-Net, due to the huge amount of 3D MRI data and high computational complexity of 3D convolution, results in significant memory consumption. To address the dependence of 3D MRI reconstruction on high computing resources, we propose an efficient 3D MRI Slice-to-Volume and Fusion Reconstruction (SVFR) method, which reduces the memory consumption requirements by 40% compared to 3D U-Net. Specifically, the proposed method integrates attention-based reconstruction and fusion models into a unified framework. For saving computational resources, instead of directly processing 3D data, we select 2D undersampled slices from three mutually orthogonal directions, and introduce the pretrained 2D Vision Transformer into MRI reconstruction field, reconstructing 3D MR images from 2D slices. In addition, for compensating the loss of spatial details between adjacent slices caused by the process of reconstructing slices, we employ a volume-wise fusion model to extract deep features of reconstructed 3D MR images along original three directions and fuse them on a spatial level, preserving finer spatial details. The experimental results on large 3D multi-coil brain <em>k</em>-space dataset and Stanford Fullysampled 3D FSE Knees dataset clearly demonstrate that the proposed method exhibits excellent reconstruction performance and efficiency under various accelerations.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"110 ","pages":"Article 108071"},"PeriodicalIF":4.9,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144147454","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}

{"title":"Spatial-frequency fusion for retinal vessel segmentation","authors":"Weiwei Song ,&nbsp;Ming Xu ,&nbsp;Haixing Li ,&nbsp;Xiaosheng Yu","doi":"10.1016/j.bspc.2025.108054","DOIUrl":"10.1016/j.bspc.2025.108054","url":null,"abstract":"<div><div>The segmentation of retinal blood vessels is clinically significant for diagnosing many ocular disorders and can assist in identifying multiple medical conditions, including diabetes, atherosclerosis, and cardiovascular disease. Therefore, accurate identification of the retinal blood vessels in the fundus can significantly aid physicians in diagnosing and treating their patients’ conditions. In this paper, we propose a retinal blood vessel segmentation method that combines the spatial and frequency domains. Existing CNN methods obtain local features by using convolutional operations in the spatial domain, and are not capable enough in obtaining global spatial feature information. Therefore, we introduce a Fourier transform to obtain global information and learn the long-distance distribution of blood vessels. In the frequency domain, we designed a multiscale Gaussian high-pass filter to adaptively enhance the edge features of blood vessels of different scales. Since frequency domain information is more concerned with global dependencies and spatial information is more capable of capturing local detailed features, the fusion of frequency and spatial domains can effectively capture the general trends and complex details within the hidden layer space. In order to assess the model’s efficacy, we conducted tests using the pre-existing DRIVE and CHASE_DB1 datasets. Our accuracy achieved 96.90 and 97.81 respectively, and a sensitivity of 83.80 was obtained for the DRIVE dataset. By observing the segmented image, our segmentation is more accurate, clearer, and noise-free than the results of other proposed methods.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"110 ","pages":"Article 108054"},"PeriodicalIF":4.9,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144137711","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}

{"title":"Spectral feature extraction using the UFIR iterative smoother algorithm for ECG signal classification","authors":"Guillermina Vivar-Estudillo ,&nbsp;R.J. Pérez Chimal ,&nbsp;J.U. Muñoz-Minjares ,&nbsp;Oscar Ibarra-Manzano ,&nbsp;Carlos Lastre-Domínguez","doi":"10.1016/j.bspc.2025.108007","DOIUrl":"10.1016/j.bspc.2025.108007","url":null,"abstract":"<div><div>Heart diseases are the leading cause of death worldwide. One effective non-invasive method for diagnosing heart-related conditions is the analysis of Electrocardiogram (ECG) recordings. These recordings capture the shape of the primary ECG waves, facilitating the automated detection of various pathologies. However, the accuracy of these measurements can be affected by noise or artifacts that occur during the ECG acquisition process. Although many techniques have been proposed to address this issue, there remains a need to improve the precision of automatic ECG signal detection and classification. Our study aimed to reduce noise and extract features from ECG signals associated with arrhythmia, congestive heart failure, and normal sinus rhythm. We evaluated the performance of the Unbiased Finite Impulse Response (UFIR) smoother by comparing it with other techniques, using the root Mean Square Error (RMSE) under various noise levels. Our findings highlighted the significant advantages of the UFIR technique. In addition, we conducted tests using Analysis of Variance (ANOVA) and Kruskal–Wallis analysis to explore the time–frequency domain features obtained from the Short-Time Fourier Transform (STFT) of the states produced by the UFIR smoother. These enhancements improved the performance of the classification model. The results indicated that machine learning techniques based on optimized neural networks achieved impressive metrics, including an F1-score of 0.96, a precision of 95.65%, an accuracy of 93.39%, and Cohen’s kappa of 0.90. Notably, the states of ECG signals provided by the UFIR smoother offer features that could significantly enhance the diagnosis of these pathologies.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"110 ","pages":"Article 108007"},"PeriodicalIF":4.9,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144138167","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}

{"title":"Pixel attention meets M-shaped networks: A cutting-edge AI solution for diabetic retinopathy classification and stroke risk prediction","authors":"Ahmed A. Alsheikhy ,&nbsp;Tawfeeq Shawly ,&nbsp;Yahia Said ,&nbsp;Aws I. AbuEid ,&nbsp;Abdulrahman A. Alzahrani ,&nbsp;Abdulrahman A. Alshdadi ,&nbsp;Hossam E. Ahmed","doi":"10.1016/j.bspc.2025.108110","DOIUrl":"10.1016/j.bspc.2025.108110","url":null,"abstract":"<div><div>Visual impairment is a major global health concern, with Diabetic Retinopathy (DR) recognized as a leading cause of vision loss and blindness among individuals with diabetes. Despite its widespread occurrence, the early detection of DR poses significant challenges due to the subtlety of initial symptoms, the requirement for expert analysis of retinal images, and the drawbacks of traditional diagnostic techniques, which can be time-consuming, subjective, and susceptible to human error. DR impacts the retina and the ocular blood vessels, making timely and accurate diagnosis crucial to prevent irreversible damage. However, recent advancements in Artificial Intelligence (AI) have opened up new avenues in medical diagnostics, offering sophisticated approaches for the detection and classification of DR with impressive accuracy. This article introduces a Pixel Attention-based M−Shaped Architecture (PAM), an innovative AI-powered diagnostic tool designed to enhance the detection and classification of DR. The PAM system consists of two key components: 1) a Pixel Attention (PIAT) network that aids in the precise identification of abnormalities in retinal blood vessels, and 2) an M−shaped neural network architecture that delivers strong segmentation and classification capabilities. Trained and validated on three diverse benchmark datasets with over 35,000 fundus images spanning multiple DR severity levels, PAM exhibits exceptional adaptability across different levels of DR severity (mild, moderate, severe, and proliferative), achieving state-of-the-art results with an accuracy of 98.73%, precision of 98.82%, sensitivity of 98.67%, specificity of 98.72%, F1-score of 99.13%, and a Dice coefficient of 98.74%. Comparative studies demonstrate that PAM outperforms current methodologies, especially in terms of accuracy and F1-score. Its key advantage is its ability to offer healthcare professionals a scalable, efficient, and dependable resource for improving clinical decision-making. Moreover, by focusing on pixel-level vascular pathology, PAM can simultaneously yield insights into systemic microvascular health, specifically functioning as an early-warning tool for stroke risk. This dual-purpose utility positions PAM as a powerful asset for telemedicine, broad screening programs, and AI-enhanced healthcare systems, where a single retinal exam can address both ocular and cerebrovascular health. By enabling timely and accurate diagnosis of DR and proactively recognizing individuals at elevated risk for stroke, PAM can significantly reduce vision loss associated with diabetes and aid in stroke prevention. This approach effectively connects advancements in AI with the clinical requirements of ophthalmology and neurology. Current efforts involve implementing PAM on mobile devices and performing external clinical validations to confirm its applicability in real-world settings and its impact across multiple domains.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"110 ","pages":"Article 108110"},"PeriodicalIF":4.9,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144147453","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}

{"title":"Unsupervised deformable registration model based on multi-scale pyramid and accurate similarity measurement","authors":"Ping Jiang,&nbsp;Wenjian Qin,&nbsp;Sijia Wu,&nbsp;Yaoqin Xie","doi":"10.1016/j.bspc.2025.108089","DOIUrl":"10.1016/j.bspc.2025.108089","url":null,"abstract":"<div><div>The deformable registration is a critical task in medical image processing. Due to significant differences in texture patterns and intensity information between modalities, current multimodal registration algorithms fail to extract multimodal features accurately and lack effective similarity measures in complex regions. To address these challenges, we propose a multilevel pyramid large deformation multimodality registration elastic network (MPLD). The framework adopts a global-to-local strategy for registration and is divided into three stages: level0 and level2 stage (global registration stage) and level1 stage (local registration stage). We propose an accurate similarity measurement evaluator to measure the spatial difference between two images, this method combines morphological and deep learning, and then optimize registration by minimizing the errors predicted by the evaluator. In addition, we propose a pyramid multi-level registration network (PM-Net), the module includes two independent encoders to extract image features of different modes, and share the same decoder, using progressive deformation field estimation in the decoder. The proposed method was validated on publicly available datasets LPBA40, OASIS, and hospitals clinical CT/MR data. In clinical data registration, our method achieved an average DSC of 0.816 ± 0.016, average ASSD of 0.894 ± 0.128 mm, average Std. Jacobian of 0.289 ± 0.012. Our algorithm achieved a higher registration accuracy compared with state-of-the-art registration methods. This method adopts a coarse-to-fine strategy for progressive deformation field prediction and leverages multi-scale feature aggregation to enhance feature extraction capability. It effectively handles large deformation registration tasks, and comparative experiments confirm the superior registration performance and generalization ability.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"110 ","pages":"Article 108089"},"PeriodicalIF":4.9,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144138454","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}