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Progress of research in the application of ultrasound technology for the treatment of Alzheimer’s disease

Neural Regeneration Research 2025年第10期20卷 2823-2837页

作者： Qiuquan Cai Lianghui Meng Meina Quan Ling Wang Jing Ren Chenguang Zheng Jiajia Yang Dong Ming Academy of Medical Engineering and Translational Medicine Tianjin UniversityTianjinChina Innovation Center for Neurological Disorders Xuanwu HospitalCapital Medical UniversityBeijingChina Brain-Computer Interaction and Human-Machine Fusion Haihe Laboratory Tianjin UniversityTianjinChina Tianjin Key Laboratory of Brain Science and Neural Engineering Tianjin UniversityTianjinChina

Alzheimer’s disease is a common neurodegenerative disorder defined by decreased reasoning abilities,memory loss,and cognitive *** presence of the blood-brain barrier presents a major obstacle to the development of effective drug therapies for Alzheimer’s *** use of ultrasound as a novel physical modulation approach has garnered widespread attention in recent *** a safe and feasible therapeutic and drug-delivery method,ultrasound has shown promise in improving cognitive *** article provides a summary of the application of ultrasound technology for treating Alzheimer’s disease over the past 5 years,including standalone ultrasound treatment,ultrasound combined with microbubbles or drug therapy,and magnetic resonance imaging-guided focused ultrasound *** is placed on the benefits of introducing these treatment methods and their potential *** found that several ultrasound methods can open the blood-brain barrier and effectively alleviate amyloid-βplaque *** believe that ultrasound is an effective therapy for Alzheimer’s disease,and this review provides a theoretical basis for future ultrasound treatment methods.

关键词： Alzheimer’s disease blood-brain barrier drugs magnetic resonance imaging-guided focused ultrasound microbubbles scanning ultrasound ultrasound ultrasound stimulation

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MixSSC: Forward-Backward Mixture for Vision-based 3D Semantic Scene Completion

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Wang, Meng Ding, Yan Liu, Yumeng Qin, Yunchuan Li, Ruihui Tang, Zhuo The College of Computer Science and Electronic Engineering Hunan University Changsha410082 China Beijing Key Laboratory of Human-Computer Interaction Institute of Software Chinese Academy of Sciences Beijing100190 China

Vision-based semantic scene completion task aims to predict dense geometric and semantic 3D scene representations from 2D images. However, 3D modeling from a single view is an ill-posed problem, limited by the field of view and occlusion problems caused by image input. Moreover, existing methods tend to produce erroneous scene hallucinations and overly smooth boundary segmentation due to a lack of information. To address this problem, we propose MixSSC, which mixes the sparsity of forward projection with the denseness of depth-prior backward projection. The aim is to use sparse features to fill information-poor regions and dense features to enhance visible regions. Specifically, we develop the forward-backward mixture module, which enables the generation of scene mixture voxel representation by leveraging the benefits of both forward and backward projection. Subsequently, we design the semantic-spatial fusion module, which utilizes a coarse-to-fine approach to process mixture voxel features at the semantic-spatial level. Extensive experimental results on the SemanticKITTI, SSCBench-KITTI-360 and nuScenes datasets demonstrate the superiority of MixSSC. © 1991-2012 IEEE.

关键词： Semantic Segmentation

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Recognizing Adenoid Hypertrophy from Facial Images with Multi-scale Feature Fused State Space Model 18th

Recognizing Adenoid Hypertrophy from Facial Images with Mu...

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18th Chinese Conference on Biometric Recognition, CCBR 2024

作者： Ma, Shuai He, Jinrong Wang, Yao Bi, Yingzhou Yang, Li College of Mathematics and Computer Science Yan’an University Yan’an716000 China Yan’an University Affiliated Hospital Yan’an716000 China Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision Nanning Normal University Nanning530100 China School Hospital Yan’an University Yan’an716000 China

ISBN: (纸本)9789819610709

Early diagnosis of pediatric adenoidal hypertrophy is crucial for timely identification of respiratory obstructions, thereby preventing declines in sleep quality that can lead to impaired growth, development, and cognitive function. Currently, the most common clinical diagnostic methods involve nasopharyngeal endoscopy and X-ray imaging assessments, which are time-consuming, labor-intensive, and costly. This paper presents an intelligent diagnostic model based on natural light facial images, employing a state space model (SSM). The model combines the local feature extraction capability of convolutional layers with the SSM’s ability to capture long-range dependencies for facial feature extraction. Furthermore, it incorporates a feature fusion strategy to enhance the features of facial images. Experimentation on a self-constructed dataset of adenoid facies images demonstrated that the model achieves an accuracy of 86.55% and an F1-score of 86.21%. The data and code are available at https://***/hejinrong/AF-SSM. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Medical imaging

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DRDM: A Disentangled Representations Diffusion Model for Synthesizing Realistic Person Images

DRDM: A Disentangled Representations Diffusion Model for Syn...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Huang, Enbo Zhang, Yuan Huang, Faliang Zhang, Guangyu Liu, Yang Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University Nanning China School of Computer and Information Engineering Nanning Normal University Nanning China College of Mathematics and Informatics South China Agricultural University Guangzhou China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China

ISBN: (纸本)9798350368741

Person image synthesis with controllable body poses and appearances is an essential task owing to the practical needs in the context of virtual try-on, image editing and video production. However, existing methods face significant challenges with details missing, limbs distortion and the garment style deviation. To address these issues, we propose a Disentangled Representations Diffusion Model (DRDM) to generate photo-realistic images from source portraits in specific desired poses and appearances. First, a pose encoder is responsible for encoding pose features into a high-dimensional space to guide the generation of person images. Second, a body-part subspace decoupling block (BSDB) disentangles features from the different body parts of a source figure and feeds them to the various layers of the noise prediction block, thereby supplying the network with rich disentangled features for generating a realistic target image. Moreover, during inference, we develop a parsing map-based disentangled classifier-free guided sampling method, which amplifies the conditional signals of texture and pose. Extensive experimental results on the Deepfashion dataset demonstrate the effectiveness of our approach in achieving pose transfer and appearance control. The associated project can be found at https://***/lovemusiceb/DRDM. © 2025 IEEE.

关键词： Diffusion Model Disentangled Representation human Parsing Person image synthesis

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StructVizor: Interactive Profiling of Semi-Structured Textual Data 25

StructVizor: Interactive Profiling of Semi-Structured Textua...

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Proceedings of the 2025 CHI Conference on human Factors in Computing Systems

作者： Yanwei Huang Yan Miao Di Weng Adam Perer Yingcai Wu State Key Lab of CAD&CG Zhejiang University Hangzhou Zhejiang China School of Software Technology Zhejiang University Ningbo Zhejiang China Human-Computer Interaction Institute Carnegie Mellon University Pittsburgh Pennsylvania USA

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DRDM: A Disentangled Representations Diffusion Model for Synthesizing Realistic Person Images

DRDM: A Disentangled Representations Diffusion Model for Syn...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Enbo Huang Yuan Zhang Faliang Huang Guangyu Zhang Yang Liu Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision Nanning Normal University Nanning China School of Computer and Information Engineering Nanning Normal University Nanning China College of Mathematics and Informatics South China Agricultural University Guangzhou China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Person image synthesis with controllable body poses and appearances is an essential task owing to the practical needs in the context of virtual try-on, image editing and video production. However, existing methods face significant challenges with details missing, limbs distortion and the garment style deviation. To address these issues, we propose a Disentangled Representations Diffusion Model (DRDM) to generate photorealistic images from source portraits in specific desired poses and appearances. First, a pose encoder is responsible for encoding pose features into a high-dimensional space to guide the generation of person images. Second, a body-part subspace decoupling block (BSDB) disentangles features from the different body parts of a source figure and feeds them to the various layers of the noise prediction block, thereby supplying the network with rich disentangled features for generating a realistic target image. Moreover, during inference, we develop a parsing map-based disentangled classifier-free guided sampling method, which amplifies the conditional signals of texture and pose. Extensive experimental results on the Deepfashion dataset demonstrate the effectiveness of our approach in achieving pose transfer and appearance control. The associated project can be found at https://***/lovemusiceb/DRDM

关键词： Limbs Image synthesis Shape Clothing Semantics Production Feature extraction Diffusion models Sampling methods Speech processing

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Enhancing motor imagery EEG classification with a Riemannian geometry-based spatial filtering (RSF) method

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Neural Networks 2025年 188卷 107511页

作者： Pan, Lincong Wang, Kun Huang, Yongzhi Sun, Xinwei Meng, Jiayuan Yi, Weibo Xu, Minpeng Jung, Tzyy-Ping Ming, Dong Academy of Medical Engineering and Translational Medicine Tianjin University Tianjin 300072 China School of Precision Instruments and Optoelectronics Engineering Tianjin University Tianjin 300072 China Haihe Laboratory of Brain-computer Interaction and Human-machine Integration Tianjin 300392 China Tianjin Key Laboratory of Brain Science and Neuroengineering Tianjin 300072 China Beijing Machine and Equipment Institute Beijing 100192 China Swartz Center for Computational Neuroscience University of California San DiegoCA92093 United States

Motor imagery (MI) refers to the mental simulation of movements without physical execution, and it can be captured using electroencephalography (EEG). This area has garnered significant research interest due to its substantial potential in brain-computer interface (BCI) applications, especially for individuals with physical disabilities. However, accurate classification of MI EEG signals remains a major challenge due to their non-stationary nature, low signal-to-noise ratio, and sensitivity to both external and physiological noise. Traditional classification methods, such as common spatial pattern (CSP), often assume that the data is stationary and Gaussian, which limits their applicability in real-world scenarios where these assumptions do not hold. These challenges highlight the need for more robust methods to improve classification accuracy in MI-BCI systems. To address these issues, this study introduces a Riemannian geometry-based spatial filtering (RSF) method that projects EEG signals into a lower-dimensional subspace, maximizing the Riemannian distance between covariance matrices from different classes. By leveraging the inherent geometric properties of EEG data, RSF enhances the discriminative power of the features while maintaining robustness against noise. The performance of RSF was evaluated in combination with ten commonly used MI decoding algorithms, including CSP with linear discriminant analysis (CSP-LDA), Filter Bank CSP (FBCSP), Minimum Distance to Riemannian Mean (MDM), Tangent Space Mapping (TSM), EEGNet, ShallowConvNet (sCNN), DeepConvNet (dCNN), FBCNet, Graph-CSPNet, and LMDA-Net, using six publicly available MI-BCI datasets. The results demonstrate that RSF significantly improves classification accuracy and reduces computational time, particularly for deep learning models with high computational complexity. These findings underscore the potential of RSF as an effective spatial filtering approach for MI EEG classification, providing new insight

关键词： Covariance matrix

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Tilting-Twisting-Rolling: a pen-based technique for compass geometric construction

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Science China(Information Sciences) 2017年第5期60卷 246-251页

作者： Fei LYU Feng TIAN Guozhong DAI Hongan WANG State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences School of Digital Media and Design Arts Beijing University of Posts and Telecommunications Beijing Key Lab of Human-Computer Interaction Institute of Software Chinese Academy of Sciences

This paper presents a new pen-based technique, Tilting-Twisting-Rolling, to support compass geometric construction. By leveraging the 3D orientation information and 3D rotation information of a pen, this technique allows smooth pen action to complete multi-step geometric construction without switching task *** from a user study show this Tilting-Twisting-Rolling technique can improve user performance and user experience in compass geometric construction.

关键词： Tilting-Twisting-Rolling pen-based tool virtual compass geometry construction interaction

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Corrigendum to "DSTF: A diversified spatio-temporal feature extraction model for traffic flow prediction" [Neurocomputing 621 (2025) 129280] (Neurocomputing (2025) 621, (S0925231224020514), (10.1016/***.2024.129280))

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Neurocomputing 2025年 638卷

作者： Wang, Xing Wang, Xiaojun Huang, Faliang Zou, Fumin Liao, Lyuchao Zeng, Ruihao Fujian Normal University College of Computer and Cyber Security China Fujian Normal University Digital Fujian Institute of Big Data Security Technology China Nanning Normal University Guangxi Key Lab of Human-machine Interaction and Intelligent Decision China Fujian University of Technology Fujian Key Laboratory of Automotive Electronic and Electrical Drive Technology China The University of Sydney School of Civil Engineering Sydney Australia

The authors regret to inform that: (1) During the resubmission stage of the revised manuscript, in compliance with reviewers' recommendations to show standard deviations in the experimental results (In Section 5. Experiment implementation), a batch replacement operation (intended to replace "+" with "±" for data formatting) inadvertently replaced the "+" symbols in the mathematical formulas and the explanatory text with "±" symbols (In Section 3. Problem statement and Section 4. Methodology). (2) In proof stage, we sincerely apologize for failing to identify this issue during the final proofreading stage. Correction statement and details: As this error may affect readers' understanding, we request a corrigendum to rectify this error. In Section 3. Problem statement and Section 4. Methodology, all instances of "±" symbols within mathematical formulas and their explanatory text should be reverted to the original "+" symbols. The authors would like to apologize for any inconvenience caused. © 2025 The Authors

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Trajectory prediction model for crossing-based target selection

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Virtual Reality & Intelligent Hardware 2019年第3期1卷 330-340页

作者： Hao ZHANG Jin HUANG Feng TIAN Guozhong DAI Hongan WANG Beijing Key Lab of Human-Computer Interaction Institute of SoftwareChinese Academy of SciencesBeijing 100190China State Key Lab of Computer Science Institute of SoftwareChinese Academy of SciencesBeijing 100190China

Background Crossing-based target selection motion may attain less error rates and higher interactive speed in some *** of the research in target selection fields are focused on the analysis of the interaction ***,as trajectories play a much more important role in crossing-based target selection compared to the other interactive techniques,an ideal model for trajectories can help computer designers make predictions about interaction results during the process of target selection rather than at the end of the whole *** In this paper,a trajectory prediction model for crossing based target selection tasks is proposed by taking the reference of a dynamic model *** Simulation results demonstrate that our model performed well with regard to the prediction of trajectories,endpoints and hitting time for target-selection motion,and the average error of trajectories,endpoints and hitting time values were found to be 17.28%,2.73mm and 11.50%,respectively.

关键词： Target selection Crossing-based selection Trajectory prediction

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