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2023 IEEE International Ultrasonics Symposium, IUS 2023

作者： Chen, Hongbo Kumaralingam, Logiraj Li, Jiawen Punithakumar, Kumaradevan Le, Lawrence H. Zheng, Rui University of Alberta Department of Radiology and Diagnostic Imaging EdmontonAB Canada ShanghaiTech University School of Information Science and Technology Shanghai China University of Alberta Department of Biomedical Engineering EdmontonAB Canada Shanghai Engineering Research Center of Energy Efficient and Custom Ai Ic Shanghai China

ISBN: (纸本)9798350346459

Accurate 3D geometric shapes of carotid arteries are imperative for the three-dimensional (3D) ultrasound (US) imaging to clinically assess the carotid atherosclerosis (CA). However, the traditional surface reconstruction method, such as iso-surface (ISO-SURF), suffers from the image noise, voxel resolution and additional processing. In this paper, we introduce the neural implicit representation based on the deep learning network for the 3D surface reconstruction of media-adventitia boundary (MAB), plaque, and lumen-intima boundary (LIB) together. The unsigned distance functions are learned by the network to generate the mesh. For the validation, six volumes were simulated in carotid shape with MAB and LIB, and random noise was added around the boundaries. The results showed that experiments on six simulated volumes illustrated better performance than ISO-SURF, with 47%, 36%, and 55% decrease in Chamfer distance, average absolute distance, and Hausdorff distance, respectively. The visualization results from the CA clinical data revealed a smoother and more continuous geometric surface than ISO-SURF. The comparison result has shown the potential of the proposed method to examine vascular pathologies in the future. © 2023 IEEE.

关键词： Diseases

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Differences in left and right carotid plaque vulnerability in patients with bilateral carotid plaques:a CARE-II study

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Stroke & Vascular Neurology 2023年第4期8卷 284-291页

作者： Sai Shao Honglu Shi Guangbin Wang Rui Li Qinjian Sun Bin Yao Hiroko Watase Daniel S Hippe Chun Yuan Xihai Zhao Department of Radiology Shandong Provincial Hospital Affiliated to Shandong First Medical UniversityJinanShandongChina Department of Medical Imaging and Intervention Shandong Provincial Hospital Affiliated to Shandong First Medical UniversityJinanShandongChina Center for Biomedical Imaging Research Department of Biomedical EngineeringTsinghua University School of MedicineBeijingChina Department of Neurology Shandong Provincial Hospital Affiliated to Shandong First Medical UniversityJinanShandongChina Department of Radiology The Second Affiliated Hospital of Nanchang UniversityNanchangJiangxiChina Department of Surgery University of WashingtonSeattleWashingtonUSA Department of Radiology University of WashingtonSeattleWashingtonUSA

Background and purpose Atherosclerosis is a very complex process influenced by various systemic and local ***,in patients with bilateral carotid plaques(BCPs),there may be differences in carotid plaque vulnerability between the *** aimed to investigate the differences in BCP characteristics in patients with BCPs using magnetic resonance vessel wall imaging(MR-VWI).Methods Participants with BCPs were selected for subanalysis from a multicentre study of Chinese Atherosclerosis Risk Evaluation *** measured carotid plaque burden,identified each plaque component and measured their volume or area bilaterally on *** comparisons of the burden and components of BCPs were *** In all,540 patients with BCPs were eligible for *** with the right carotid artery(CA),larger mean lumen area(p<0.001),larger mean wall area(p=0.025),larger mean total vessel area(p<0.001)and smaller normalised wall index(p=0.006)were found in the left *** plaque components,only the prevalence of lipid-rich necrotic core(LRNC)in the left CA was higher(p=0.026).For patients with a vulnerable plaque component coexisting on both sides,only the intraplaque haemorrhage(IPH)volume(p=0.011)was significantly greater in the left CA than in the right *** There were asymmetries in plaque growth and evolution between *** left carotid plaques were more likely to have larger plaque burden,higher prevalence of LRNC and greater IPH volume,which may contribute to the lateralisation of ischaemic stroke in the cerebral hemispheres.

关键词： carotid patients plaques

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Design of 12-Lead Electrocardiogram Smart Clothing Based on Capacitive Sensing Technique

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IEEE Sensors Journal 2024年第10期24卷 16826-16835页

作者： Chang, Wei-Ting Lin, Bor-Shing Tsai, Wei-Lun Chen, Heng-Yin Liu, Chengyu Lin, Bor-Shyh Chi Mei Medical Center The Department of Cardiology Tainan71004 Taiwan National Taipei University Department of Computer Science and Information Engineering New Taipei City237303 Taiwan Institute of Imaging and Biomedical Photonics National Yang Ming Chiao Tung University Tainan71150 Taiwan Industrial Technology Research Institute System Design and Integration Department Hsinchu31057 Taiwan Southeast University School of Instrument Science and Engineering Nanjing210096 China

A 12-lead electrocardiogram (ECG) is one of the most commonly used tools for evaluating cardiovascular diseases (CVDs). In clinical, the conventional Ag/AgCl electrodes with conductive gel are usually used to monitor ECG, but they may encounter gel drying and skin allergic reaction problems for long-term ECG monitoring. In order to improve the above issue, several dry electrodes, made by conductive materials, was proposed in the design of wearable devices, but they still encounter the problems of electrode shifting and stretching, and the risk of skin irritation. Capacitive electrode can access biopotential without contacting skin directly and can completely avoid skin irritation. However, for capacitive electrode, how to maintain a good electrode-skin contacting condition is still a challenge due to electrode fixation problem, in particular in multichannel ECG measurement. In this study, a 12-lead ECG smart clothing based on capacitive sensing technology was proposed to monitor ECG in daily life. To improve the electrode-skin contacting condition, elastic conductive foams were used as the electrode plate to effectively fit the rough body contours. Moreover, a specific wearable belt mechanism was also designed to improve the convenience of use, and it could also provide good friction and proper pressure to reduce the electrode slippage and maintain a good electrode-skin contact condition. The experimental results showed that the proposed smart clothing could stably access the good ECG signal quality under different motion levels, and the specific ECG features corresponding to different CVDs could also be reflected on the measured ECG signal in clinical experiment. © 2001-2012 IEEE.

关键词： Electrocardiograms

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A Progressive Single-Modality to Multi-modality Classification Framework for Alzheimer’s Disease Sub-type Diagnosis 7th

A Progressive Single-Modality to Multi-modality Classificat...

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7th International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2024, Held in Conjunction with 27th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2024

作者： Liu, Yuxiao Liu, Mianxin Zhang, Yuanwang Sun, Kaicong Shen, Dinggang School of Biomedical Engineering and State Key Laboratory of Advanced Medical Materials and Devices ShanghaiTech University Shanghai201210 China Shanghai Artificial Intelligence Laboratory Shanghai200232 China Shanghai United Imaging Intelligence Co. Ltd. Shanghai200230 China Shanghai Clinical Research and Trial Center Shanghai201210 China

ISBN: (纸本)9783031787607

The current clinical diagnosis framework of Alzheimer’s disease (AD) involves multiple modalities acquired from multiple diagnosis stages, each with distinct usage and cost. Previous AD diagnosis research has predominantly focused on how to directly fuse multiple modalities for an end-to-end one-stage diagnosis, which practically requires a high cost in data acquisition. Moreover, a significant part of these methods diagnose AD without considering clinical guideline and cannot offer accurate sub-type diagnosis. In this paper, by exploring inter-correlation among multiple modalities, we propose a novel progressive AD sub-type diagnosis framework, aiming to give diagnosis results based on easier-to-access modalities in earlier low-cost stages, instead of all modalities from all stages. Specifically, first, we design 1) a text disentanglement network for better processing tabular data collected in the initial stage, and 2) a modality fusion module for fusing multi-modality features separately. Second, we align features from modalities acquired in earlier low-cost stage(s) with later high-cost stage(s) to give accurate diagnosis without actual modality acquisition in later-stage(s) for saving cost. Furthermore, we follow the clinical guideline to align features at each stage for achieving sub-type diagnosis. Third, we leverage a progressive classifier that can progressively include additional acquired modalities (if needed) for diagnosis, to achieve the balance between diagnosis cost and diagnosis performance. We evaluate our proposed framework on large diverse public and in-home datasets (8280 subjects in total) and achieve superior performance over state-of-the-art methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Neurodegenerative diseases

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Optimizing Machine Learning Hyperparameters in Two-photon FLIM Image Analysis 23

Optimizing Machine Learning Hyperparameters in Two-photon FL...

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Multiphoton Microscopy in the biomedical Sciences XXIII 2023

作者： Mbogo, Brian P. Siller, Karsten H. Zhang, Jiaxin Wallrabe, Horst Alam, Shagufta Rehman Periasamy, Ammasi The W.M. Keck Center for Cellular Imaging University of Virginia VA22904 United States Department of Biology and Biomedical Engineering University of Virginia VA22904 United States Department of Research Computing University of Virginia VA22904 United States

ISBN: (纸本)9781510658738

Artificial intelligence (AI)/machine-learning (ML) algorithms have been heavily used in data processing in various biological and clinical applications. Sensitive biological signaling can be monitored using two-photon fluorescence lifetime imaging microscopy (FLIM). Lifetime fitting, processing, and analyzing FLIM data of biological specimens can be a challenging and time-consuming affair. The recently developed Fluorescence Lifetime Redox Ratio (FLIRR) focuses on tracking metabolic changes ‘before-and-after-treatment’ in live cells using only two lifetime parameters. FLIM data produces many data parameters which are all associated with drug response in living cells. To predict drug cellular responses, we have chosen the Becker & Hickl SPCImage software to fit the lifetime images and the resultant data was used in ML analysis. With the objective of achieving even more robust statistical power predicting earliest drug effects, we developed Python software and autoencoder (AE) models to analyze the multiple biophysical FLIM parameters acquired in 2p-FLIM images of drug response in cervical cancer cells. The use of systematic hyperparameter (HP) tuning shows the variation in performance across the different models, enabling the selection of the highest performing model and HPs for repetition. Our results show that our optimized multi-parameter trained AE models can statistically outperform single FLIRR time-course analysis in discriminating earliest metabolic changes following drug treatment. © 2023 SPIE.

关键词： Machine learning

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DEEP-squared:deep learning powered De-scattering with Excitation Patterning

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Light(Science & Applications) 2023年第10期12卷 2199-2214页

作者： Navodini Wijethilake Mithunjha Anandakumar Cheng Zheng Peter T.C.So Murat Yildirim Dushan N.Wadduwage Center for Advanced Imaging Faculty of Arts and SciencesHarvard UniversityCambridgeMAUSA Department of Electronic and Telecommunication Engineering University of MoratuwaMoratuwaSri Lanka Department of Mechanical Engineering Massachusetts Institute of Technology77 Massachusetts Ave.CambridgeMA 02139USA Laser Biomedical Research Center Massachusetts Institute of Technology77 Massachusetts Ave.CambridgeMA 02139USA Department of Biological Engineering Massachusetts Institute of Technology77 Massachusetts Ave.CambridgeMA 02139USA Picower Institute for Learning and Memory Massachusetts Institute of Technology77 Massachusetts Ave.CambridgeMA 02139USA Department of Neuroscience Cleveland Clinic Lerner Research InstituteClevelandOH 44195USA

Limited throughput is a key challenge in in vivo deep tissue imaging using nonlinear optical *** scanning multiphoton microscopy,the current gold standard,is slow especially compared to the widefield imaging modalities used for optically cleared or thin *** recently introduced“De-scattering with Excitation Patterning”or“DEEP”as a widefield alternative to point-scanning *** patterned multiphoton excitation,DEEP encodes spatial information inside tissue before ***,to de-scatter at typical depths,hundreds of such patterned excitations were *** this work,we present DEEP2,a deep learning-based model that can de-scatter images from just tens of patterned excitations instead of ***,we improve DEEP’s throughput by almost an order of *** demonstrate our method in multiple numerical and experimental imaging studies,including in vivo cortical vasculature imaging up to 4 scattering lengths deep in live mice.

关键词： scattering excitation scatter

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Microwave dielectric and structural characteristics in Li2O-B2O3-SiO2 glass ceramics

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Ceramics International 2025年第16期51卷 22925-22931页

作者： Qiu, Jun-Yan Tu, Chi-Shun Huang, Szu-Ting Li, Yongxiang Chen, Pin-Yi Chen, Cheng-Sao Montecillo, Rhys Feng, Kuei-Chih International Ph.D. Program in Innovative Technology of Biomedical Engineering and Medical Devices Ming Chi University of Technology New Taipei City Taiwan Department of Physics Fu Jen Catholic University New Taipei City Taiwan Department of Mechanical Engineering Ming Chi University of Technology New Taipei City Taiwan School of Engineering RMIT University MelbourneVIC3000 Australia Research Center for Intelligent Medical Devices Ming Chi University of Technology New Taipei City Taiwan Department of Mechanical Engineering Hwa Hsia University of Technology New Taipei City Taiwan

Advancements in high-frequency communication devices have emphasized miniaturization and low energy consumption. This work focuses on developing microwave dielectric materials characterized by an ultra-low dielectric constant and low dielectric loss coefficient. In this study, B2O3 and Li2O were added into the SiO2 matrix to form the LBS glass-ceramics. The B2O3-added SiO2 shows a formation of α-cristobalite phase, and the Li2O additive in the B2O3-SiO2 glass-ceramics sintered at 900 °C reveal a primary quartz phase with a secondary phase Li2Si2O5, that partially transformed into Li2SiO3 in the specimen sintered at 950 °C. The LBS12 (x = 12 wt%) show stronger Raman peaks of Li2Si2O5 and Li2SiO3, indicating increase of highly-crystallized Si-O network. As the quartz phase diminishes, Li2Si2O5 and Li2SiO3 begin to develop in the glass-ceramic matrix. Dense microstructures and well-formed crystalline grains were observed in the LBS glass-ceramics. The quality factors were primarily affected by grain size and density. The Li2Si2O5 additive in the LBS glass-ceramics can reduce dielectric permittivity (Εr) and increase Q × f, particularly at higher frequencies. A low Εr of 4.7–5, a high Q × f of 2600 GHz, and a hardness of 249 MHV were achieved in the LBS12 sintered at 900 °C. These LBS glass-ceramics exhibit potential for use in low-temperature co-fired ceramic (LTCC) applications in high-frequency communication devices. © 2025

关键词： Sintering

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Prefallkd: Pre-Impact Fall Detection Via CNN-ViT Knowledge Distillation 48

Prefallkd: Pre-Impact Fall Detection Via CNN-ViT Knowledge D...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Chi, Tin-Han Liu, Kai-Chun Hsieh, Chia-Yeh Tsao, Yu Chan, Chia-Tai National Yang Ming Chiao Tung University Department of Biomedical Engineering Taiwan Academia Sinica Research Center for Information Technology Innovation Taiwan Fu Jen Catholic University Bachelor's Program in Medical Informatics and Innovative Applications Taiwan

ISBN: (纸本)9781728163277

Fall accidents are critical issues in an aging and aged society. Recently, many researchers developed "pre-impact fall detection systems"using deep learning to support wearable-based fall protection systems for preventing severe injuries. However, most works only employed simple neural network models instead of complex models considering the usability in resource-constrained mobile devices and strict latency requirements. In this work, we propose a novel pre-impact fall detection via CNN-ViT knowledge distillation, namely PreFallKD, to strike a balance between detection performance and computational complexity. The proposed PreFallKD transfers the detection knowledge from the pre-trained teacher model (vision transformer) to the student model (lightweight convolutional neural networks). Additionally, we apply data augmentation techniques to tackle issues of data imbalance. We conduct the experiment on the KFall public dataset and compare PreFallKD with other state-of-the-art models. The experiment results show that PreFallKD could boost the student model during the testing phase and achieves reliable F1-score (92.66%) and lead time (551.3 ms). © 2023 IEEE.

关键词： Inertial measurement units Knowledge distillation Pre-impact fall detection Vision transformer Wearable sensors

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Analysis of Gait Data Based on Deep Learning to Predict Multiple Balance Scale Scores

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IEEE Sensors Journal 2024年第1期24卷 920-930页

作者： Lin, Chueh-Ho Peng, Chih-Wei Lee, I-Jung Wu, Chi-Ming Lin, Bor-Shing Taipei Medical University International Ph.D. Program in Gerontology and Long-Term Care Taipei City110 Taiwan Taipei Medical University Research Center in Nursing Clinical Practice Wan Fang Hospital Taipei City110 Taiwan Taipei Medical University School of Biomedical Engineering College of Biomedical Engineering Taipei City110 Taiwan Taipei Medical University School of Gerontology and Long-Term Care College of Nursing Taipei City110 Taiwan National Taipei University Department of Computer Science and Information Engineering New Taipei City237 Taiwan National Taipei University College of Electrical Engineering and Computer Science New Taipei City237 Taiwan

Unbalanced walking is increasingly common among older adults;therefore, routinely assessing the balance of older adults is crucial. The traditional method of assessing balance uses scales, requires the supervision of a physical therapist (PT), and is time-consuming. The present study, therefore, proposes a deep learning (DL) model architecture that combines a convolutional neural network (CNN) and a long short-term memory (LSTM) network model to predict the scores of three scales, namely the Berg balance scale (BBS), timed up and go (TUG), and single-leg stance (SLS). The gait data of 15 m of walking were collected from seven inertial measurement units (IMUs) and input into the CNN-LSTM model for evaluation. The BBS and TUG only require the participants to wear two IMUs on the left and right thighs, respectively, for accurate predictions. The mean absolute errors (MAEs) of predicting the scores of the BBS and TUG are 1.2562 and 1.4016 s, respectively;however, the MAE of score predictions of SLS is higher than that of the BBS and TUG, indicating that gait data cannot be used for assessing SLS;moreover, participants only wear one IMU on the right calf for BBS and TUG evaluations, which yield MAEs of 1.4334 and 1.5229 s, respectively. The proposed system can quickly and accurately predict the scores of the BBS and TUG. The proposed model can assist PTs with making clinical decisions. © 2001-2012 IEEE.

关键词： Long short-term memory

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Reconstruction of Plane-Wave imaging Using Dual-Domain Hybrid Neural Network

Reconstruction of Plane-Wave Imaging Using Dual-Domain Hybri...

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IEEE International Symposium on Applications of Ferroelectrics (ISAF)

作者： Tianli Wang Yu Qiang Weibao Qiu Xinyu Zhang Yuanyuan Shen Minhua Lu Xin Chen Yanyan Yu School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Paul C. Lauterbur Research Center for Biomedical Imaging Shenzhen Institute of Advanced Technology Chinese Academy of Sciences

ISBN: (数字)9798350371901

ISBN: (纸本)9798350371918

The image quality of plane wave (PW) ultrasound imaging is usually enhanced through Coherent Plane-Wave Compounding (CPWC), which utilizes multiple angles of PW for imaging. The image quality is strongly correlated with the number of PW angles, thus necessitating a trade-off between imaging time and image quality. Both radio frequency (RF) data and single-angle image reconstructed by Delay-and-Sum (DAS) contain rich information. Therefore, we designed a hybrid CNN-Transformer model that can leverage the advantages of the two types of data to improve the quality of single-angle PW images. Specifically, we designed the perception module to utilize the beamformed images as guidance to extract information from the RF signals. Compared to DAS, it achieved an improvement of 13.26 dB in CR. The experimental results demonstrated the performance of our proposed model.

关键词： Radio frequency Image quality Ultrasonic imaging Neural networks RF signals imaging Data models Data mining Image reconstruction Frequency control

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