检索结果-内蒙古大学图书馆

Deep learning with diffusion MRI as in vivo microscope reveals sex-related differences in human white matter microstructure (Vol 14, 9835, 2024)

引用

SCIENTIFIC REPORTS 2024年第1期14卷 1-11页

作者： Chen, Junbo Bayanagari, Vara Lakshmi Chung, Sohae Wang, Yao Lui, Yvonne W. Department of Electrical and Computer Engineering New York University Tandon School of Engineering 370 Jay Street 9th Floor Brooklyn NY 11201 USA Department of Biomedical Engineering New York University Tandon School of Engineering Brooklyn NY USA Center for Advanced Imaging Innovation and Research (CAI2R) Department of Radiology New York University Grossman School of Medicine New York NY USA Bernard and Irene Schwartz Center for Biomedical Imaging Department of Radiology New York University Grossman School of Medicine New York NY USA

Biological sex is a crucial variable in neuroscience studies where sex differences have been documented across cognitive functions and neuropsychiatric disorders. While gross statistical differences have been previously documented in macroscopic brain structure such as cortical thickness or region size, less is understood about sex-related cellular-level microstructural differences which could provide insight into brain health and disease. Studying these microstructural differences between men and women paves the way for understanding brain disorders and diseases that manifest differently in different sexes. Diffusion MRI is an important in vivo, non-invasive methodology that provides a window into brain tissue microstructure. Our study develops multiple end-to-end classification models that accurately estimates the sex of a subject using volumetric diffusion MRI data and uses these models to identify white matter regions that differ the most between men and women. 471 male and 560 female healthy subjects (age range, 22–37 years) from the Human Connectome Project are included. Fractional anisotropy, mean diffusivity and mean kurtosis are used to capture brain tissue microstructure characteristics. Diffusion parametric maps are registered to a standard template to reduce bias that can arise from macroscopic anatomical differences like brain size and contour. This study employ three major model architectures: 2D convolutional neural networks, 3D convolutional neural networks and Vision Transformer (with self-supervised pretraining). Our results show that all 3 models achieve high sex classification performance (test AUC 0.92–0.98) across all diffusion metrics indicating definitive differences in white matter tissue microstructure between males and females. We further use complementary model architectures to inform about the pattern of detected microstructural differences and the influence of short-range versus long-range interactions. Occlusion analysis together with

关键词：

来源：评论

学校读者我要写书评

暂无评论

High Speed Miniaturized Multiphoton Microscopy with Elliptical Beam Excitation

High Speed Miniaturized Multiphoton Microscopy with Elliptic...

引用

Optics and the Brain, BRAIN 2023 - Part of Optica Biophotonics Congress: Optics in the Life Sciences 2023

作者： Mattison, Ben Liu, Shing-Jiuan Tian, Feng Yang, Weijian Department of Biomedical Engineering University of California Davis DavisCA95616 United States Department of Electrical and Computer Engineering University of California Davis DavisCA95616 United States Biomedical Engineering Graduate Group University of California Davis DavisCA95616 United States

We demonstrate elliptical beam shaping in a miniaturized two-photon microscope enabling cellular resolution fluorescence imaging at over three times faster frame rate than point scanning over a 400 µm diameter FO... 详细信息

ISBN: (纸本)9781957171210

关键词： Beam shaping CMOS cameras Cylindrical lenses Hollow core fibers Laser scanning Multiphoton microscopy

来源：评论

学校读者我要写书评

暂无评论

Continuously Tunable Inductorless Phase Shifter Unit Cell Based on NRI-TL Metamaterial

Continuously Tunable Inductorless Phase Shifter Unit Cell Ba...

引用

16th German Microwave Conference, GeMiC 2025

作者： Lavric, Andrej Batagelj, Bostjan Antoniades, Marco A. University of Ljubljana Faculty of Electrical Engineering Slovenia Toronto Metropolitan University Department of Electrical Computer & Biomedical Engineering Canada

ISBN: (纸本)9783982039749

A simple method for the design of a continuously tunable phase shifter based on a negative-refractive-index transmission line (NRI-TL) metamaterial without the need for variable inductors is presented. The employed λ/4 transformer solution consists of only three varactor diodes and three corresponding bias resistors, which greatly simplify the design and lower the cost of the phase-shifting stage. A single unit cell of the phase shifter prototype exhibits a total phase shift of 60° over 4V at 2.4GHz and a fractional bandwidth of 37% with insertion loss of only 0.5dB. Some of the drawbacks are also discussed. © 2025 Institut fur Mikrowellen und Antennentechnik - IMA.

关键词： Metamaterials

来源：评论

学校读者我要写书评

暂无评论

A Realistic Surgical Simulator for Non-Rigid and Contact-Rich Manipulation in Surgeries with the da Vinci Research Kit

A Realistic Surgical Simulator for Non-Rigid and Contact-Ric...

引用

International Conference on Ubiquitous Robots (UR)

作者： Yafei Ou Sadra Zargarzadeh Paniz Sedighi Mahdi Tavakoli Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada Department of Electrical and Computer Engineering and the Department of Biomedical Engineering University of Alberta Edmonton Alberta Canada

ISBN: (数字)9798350361070

ISBN: (纸本)9798350361087

Realistic real-time surgical simulators play an increasingly important role in surgical robotics research, such as surgical robot learning and automation, and surgical skills assessment. Although there are a number of existing surgical simulators for research, they generally lack the ability to simulate the diverse types of objects and contact-rich manipulation tasks typically present in surgeries, such as tissue cutting and blood suction. In this work, we introduce CRESSim, a realistic surgical simulator based on PhysX 5 for the da Vinci Research Kit (dVRK) that enables simulating various contact-rich surgical tasks involving different surgical instruments, soft tissue, and body fluids. The real-world dVRK console and the master tool manipulator (MTM) robots are incorporated into the system to allow for teleoperation through virtual reality (VR). To showcase the advantages and potentials of the simulator, we present three examples of surgical tasks, including tissue grasping and deformation, blood suction, and tissue cutting. These tasks are performed using the simulated surgical instruments, including the large needle driver, suction irrigator, and curved scissor, through VR-based teleoperation.

关键词： Medical robotics Fluids Deformation Instruments Surgery Grasping Virtual reality

来源：评论

学校读者我要写书评

暂无评论

Development of an Adaptive Artifact Subspace Reconstruction Based on Hebbian/Anti-Hebbian Learning Networks for Enhancing BCI Performance

引用

IEEE Transactions on Neural Networks and Learning Systems 2024年第1期35卷 348-361页

作者： Tsai, Bo-Yu Diddi, Sandeep Vara Sankar Ko, Li-Wei Wang, Shuu-Jiun Chang, Chi-Yuan Jung, Tzyy-Ping Institute of Biomedical Engineering and the Brain Research Center National Yang Ming Chiao Tung University Hsinchu300 Taiwan Hsinchu300 Taiwan College of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu300 Taiwan Institute of Electrical and Control Engineering Institute of Biomedical Engineering Brain Research Center College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Department of Electronics and Electrical Engineering Hsinchu300 Taiwan Kaohsiung Medical University Department of Biomedical Science and Environment Biology Drug Development and Value Creation Research Center Kaohsiung807 Taiwan Taipei Veterans General Hospital Taipei11217 Taiwan National Yang Ming Chiao Tung University Brain Research Center School of Medicine Taipei112 Taiwan University of California San Diego Swartz Center for Computational Neuroscience San DiegoCA92093 United States

Brain-computer interface (BCI) actively translates the brain signals into executable actions by establishing direct communication between the human brain and external devices. Recording brain activity through electroencephalography (EEG) is generally contaminated with both physiological and nonphysiological artifacts, which significantly hinders the BCI performance. Artifact subspace reconstruction (ASR) is a well-known statistical technique that automatically removes artifact components by determining the rejection threshold based on the initial reference EEG segment in multichannel EEG recordings. In real-world applications, the fixed threshold may limit the efficacy of the artifact correction, especially when the quality of the reference data is poor. This study proposes an adaptive online ASR technique by integrating the Hebbian/anti-Hebbian neural networks into the ASR algorithm, namely, principle subspace projection ASR (PSP-ASR) and principal subspace whitening ASR (PSW-ASR) that segmentwise self-organize the artifact subspace by updating the synaptic weights according to the Hebbian and anti-Hebbian learning rules. The effectiveness of the proposed algorithm is compared to the conventional ASR approaches on benchmark EEG dataset and three BCI frameworks, including steady-state visual evoked potential (SSVEP), rapid serial visual presentation (RSVP), and motor imagery (MI) by evaluating the root-mean-square error (RMSE), the signal-to-noise ratio (SNR), the Pearson correlation, and classification accuracy. The results demonstrated that the PSW-ASR algorithm effectively removed the EEG artifacts and retained the activity-specific brain signals compared to the PSP-ASR, standard ASR (Init-ASR), and moving-window ASR (MW-ASR) methods, thereby enhancing the SSVEP, RSVP, and MI BCI performances. Finally, our empirical results from the PSW-ASR algorithm suggested the choice of an aggressive cutoff range of c =1-10 for activity-specific BCI applications and a moderat

关键词： Electroencephalography

来源：评论

学校读者我要写书评

暂无评论

Mutual Coupling Reduction of Miniaturized MM-wave ME-dipole Antennas using SRR Superstrate

Mutual Coupling Reduction of Miniaturized MM-wave ME-dipole ...

引用

2022 United States National Committee of URSI National Radio Science Meeting, USNC-URSI NRSM 2022

作者： Kakhki, Mehri Borhani Denidni, Tayeb A. Dadgarpour, Abdolmehdi Sebak, Abdel-Razik Antoniades, Marco A. University of Quebec Montréal Canada Concordia University Department of Electrical and Computer Engineering Montréal Canada Ryerson University Department of Electrical Computer & Biomedical Engineering Toronto Canada

ISBN: (数字)9781946815156

ISBN: (纸本)9781946815156

In this paper, the effect of Split Ring Resonator (SRR) loading on mutual coupling reduction of Magneto Electric (ME)-dipole antennas fed through printed ridge gap waveguide (PRGW) is presented for millimeter-wave 5G applications. A miniaturized ME-dipole antenna with a -10 dB impedance bandwidth of 27 to 37 GHz and a peak gain of 10 dBi at 33 GHz is designed as the elementary antenna. As a proof of concept, a two-port ME-dipole antenna with half-wavelength inter-element spacing is loaded with a single SRR layer in the elevation plane. The simulated results demonstrate that the SRR superstrate reduces the mutual coupling by up to 12 dB while maintaining the impedance bandwidth of the antenna. Moreover, the realized gain of the antenna is improved in the upper bandwidth by up to 2.4 dBi due to the SRR's phase compensation on the antenna aperture. © 2022 USNC-URSI.

关键词： Bandwidth

来源：评论

学校读者我要写书评

暂无评论

Virtual Staining,Segmentation,and Classification of Blood Smears for Label-Free Hematology Analysis

引用

biomedical engineering Frontiers 2022年第1期3卷 309-322页

作者： Nischita Kaza Ashkan Ojaghi Francisco E.Robles School of Electrical and Computer Engineering Georgia Institute of TechnologyAtlantaGeorgiaUSA Wallace H.Coulter Department of Biomedical Engineering Georgia Institute of Technology and Emory UniversityAtlantaGeorgiaUSA

Objective and Impact *** present a fully automated hematological analysis framework based on single-channel(single-wavelength),label-free deep-ultraviolet(UV)microscopy that serves as a fast,cost-effective alternative to conventional hematology *** analysis is essential for the diagnosis and monitoring of several diseases but requires complex systems operated by trained personnel,costly chemical reagents,and lengthy ***-free techniques eliminate the need for staining or additional preprocessing and can lead to faster analysis and a simpler *** this work,we leverage the unique capabilities of deep-UV microscopy as a label-free,molecular imaging technique to develop a deep learning-based pipeline that enables virtual staining,segmentation,classification,and counting of white blood cells(WBCs)in single-channel images of peripheral blood *** train independent deep networks to virtually stain and segment grayscale images of *** segmented images are then used to train a classifier to yield a quantitative five-part WBC *** virtual staining scheme accurately recapitulates the appearance of cells under conventional Giemsa staining,the gold standard in *** trained cellular and nuclear segmentation networks achieve high accuracy,and the classifier can achieve a quantitative five-part differential on unseen test *** proposed automated hematology analysis framework could greatly simplify and improve current complete blood count and blood smear analysis and lead to the development of a simple,fast,and low-cost,point-of-care hematology analyzer.

关键词： networks classifier Blood

来源：评论

学校读者我要写书评

暂无评论

Jointly Optimized Spatial Histogram UNET Architecture(JOSHUA)for Adipose Tissue Segmentation

引用

biomedical engineering Frontiers 2022年第1期3卷 248-261页

作者： Joshua K.Peeples Julie F.Jameson Nisha M.Kotta Jonathan M.Grasman Whitney L.Stoppe Alina Zare Department of Electrical and Computer Engineering University of FloridaGainesvilleFL 32611USA Department of Chemical Engineering University of FloridaGainesvilleFL 32611USA J.Crayton Pruitt Family Department of Biomedical Engineering University of FloridaGainesvilleFL 32611USA Department of Biomedical Engineering New Jersey Institute of TechnologyUniversity HeightsNewarkNJ 07102USA

*** aim to develop a machine learning algorithm to quantify adipose tissue deposition at surgical sites as a function of biomaterial *** *** our knowledge,this study is the first investigation to apply convolutional neural network(CNN)models to identify and segment adipose tissue in histological images from silk fibroin biomaterial *** designing biomaterials for the treatment of various soft tissue injuries and diseases,one must consider the extent of adipose tissue *** this work,we analyzed adipose tissue accumulation in histological images of sectioned silk fibroin-based biomaterials excised from rodents following subcutaneous implantation for 1,2,4,or 8 *** strategies for quantifying adipose tissue after biomaterial implantation are often tedious and prone to human bias during *** used CNN models with novel spatial histogram layer(s)that can more accurately identify and segment regions of adipose tissue in hematoxylin and eosin(H&E)and Masson’s trichrome stained images,allowing for determination of the optimal biomaterial *** compared the method,Jointly Optimized Spatial Histogram UNET Architecture(JOSHUA),to the baseline UNET model and an extension of the baseline model,attention UNET,as well as to versions of the models with a supplemental attention-inspired mechanism(JOSHUA+and UNET+).*** inclusion of histogram layer(s)in our models shows improved performance through qualitative and quantitative *** results demonstrate that the proposed methods,JOSHUA and JOSHUA+,are highly beneficial for adipose tissue identification and *** new histological dataset and code used in our experiments are publicly available.

关键词： injuries implantation Spatial

来源：评论

学校读者我要写书评

暂无评论

Reconstruct Dense Live-Cell Microscopy Images via Learning Continuous Fluorescence Field 22

Reconstruct Dense Live-Cell Microscopy Images via Learning C...

引用

22nd IEEE International Symposium on biomedical Imaging, ISBI 2025

作者： Cao, Jianfeng Duan, Bin Yan, Hong School of Biomedical Engineering Harbin Institute of Technology Shenzhen China Illinois Institute of Technology Department of Computer Science United States City University of HongKong Department of Electrical Engineering Hong Kong

ISBN: (纸本)9798331520526

Live-cell imaging plays an increasingly important role in developmental biology and drug discovery. The phototoxity of laser light, however, inevitably induces a tradeoff between image quality and cell viability. While learning-based denoising models have been proposed for improving noisy low-quality images, it is practically challenging to obtain high-quality training reference in live-cell imaging. In this work, we present a reference-free framework MicroNeRF for reconstructing dense image stack from sparse slices. By formalizing the fluorescence field as an implicit neural representation, high-quality stack can be posteriorly sampled from continuous manifold. Additionally, we address the interference among fluorescent signals through an adaptive learnable point spread function. Experimental results demonstrate the superiority of MicroNeRF in sparse reconstruction and downstream segmentation task. © 2025 IEEE.

关键词： Optical transfer function

来源：评论

学校读者我要写书评

暂无评论

Inductively-Coupled MEMS Pressure Sensor

Inductively-Coupled MEMS Pressure Sensor

引用

2023 IEEE biomedical Circuits and Systems Conference, BioCAS 2023

作者： Patel, Sneh Darbasi, Sima Antoniades, Marco A. Valente, Virgilio Computer and Biomedical Engineering Toronto Metropolitan University Department of Electrical TorontoM5B 2K3 Canada

ISBN: (纸本)9798350300260

This research paper introduces a novel design of an inductive sensor based on a MEMS (Micro-Electro-Mechanical System) inductive link. The sensor comprises two identical Al inductor coils micromachined on each side of a thinned silicon wafer. The sensing coil is suspended over a 30μm cavity etched in the silicon wafer. The sensor was designed, modeled, and simulated using the ANSYS Electronics 3D electromagnetic High-Frequency Simulation Software (HFSS). Simulated results showed that the impedance of the primary coil varied from approximately 2310 Ω to 2775 Ω over a deflection range of 20 μm, indicating an average sensitivity of 23.25 Ω/μm. © 2023 IEEE.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：