Comparison of appearance time in brain between red blood cell and plasma using H215O and15O2applying positron emission tomography [0.03%]
应用正电子发射断层扫描比较H215O及15O2的红细胞和血浆在脑中的出现时间
Nobuyuki Kudomi,Takuya Kobata,Yukito Maeda et al.
Nobuyuki Kudomi et al.
The appearance time of blood components in the brain provides complementary information about cerebral microvascular dynamics. Plasma and red blood cells (RBCs) behave differently in the microcirculation: while plasma can pass through perip...
Meta-analysis of mRNA dysregulation associated with Parkinson's disease and other neurological disorders [0.03%]
与帕金森病和其他神经障碍相关的mRNA失调的荟萃分析
Tun Lin Aung,Ye Win Aung,Xiaoran Shi
Tun Lin Aung
Parkinson's disease (PD) is the second most common progressive neurodegenerative disorder, characterized by both motor and non-motor symptoms. In this study, we conducted a meta-analysis of gene expression profiles from four GEO datasets (c...
End-to-End EEG Artifact Removal Method via Nested Generative Adversarial Network [0.03%]
基于嵌套生成对抗网络的端到端EEG伪迹移除方法
Tianqi Yang,Shengsheng Cai,Dongyang Xu et al.
Tianqi Yang et al.

As physiological artifacts commonly overlap with EEG signals in both time and frequency domains, developing an effective end-to-end EEG artifact removal method is essential for a brain-computer interface (BCI) system.
Approach.
...
Cross-Section-Based Scaling Method for Material-Specific Cluster Dose Calculations - A Proof of Concept [0.03%]
基于截面的材料特定聚簇剂量计算缩放方法的概念验证
Miriam Schwarze,Hui Khee Looe,Bjoern Poppe et al.
Miriam Schwarze et al.
Cross-section data unavailability for non-water materials in track structuresimulation software necessitates nanodosimetric quantity transformationother materials. Cluster dose calculation transformation initially fromemployed water, tomass...
TF-CrossNet: A Cross-Modal Attention Fusion Network for Cardiovascular Disease Classification Using PCG and ECG Signals [0.03%]
基于PCG和ECG信号的心血管疾病分类的交叉模态注意融合网络
Xingguang Li,Yutong Hou,Kaiyao Shi et al.
Xingguang Li et al.
Electrocardiogram (ECG) and phonocardiogram (PCG) have emerged as crucial non-invasive and portable diagnostic modalities for early cardiovascular disease (CVD) screening. Despite the individual merits of these signal modalities in CVD dete...
Dose Stratification-Based Convolutional Neural Networks for Dose Distribution Prediction in Radiotherapy [0.03%]
基于剂量分层的卷积神经网络在放射治疗中进行剂量分布预测
Ye Tian,Qiuhong Wang,Liang Chen et al.
Ye Tian et al.
The fidelity of dose distribution prediction is paramount for radiotherapy planning. While existing deep learning-based methods have obtained noteworthy performance, most of them pursue the accurate prediction of global dose distribution bu...
Development of a motorized iris collimator for kilovoltage x-ray radiotherapy [0.03%]
千伏X射线放射治疗电动光圈准直器的发展
Olivia Masella,William Canthal,Lane Aaron Braun et al.
Olivia Masella et al.
To design and build a motorized iris collimator suitable for a novel low-cost kilovoltage dual-robot radiotherapy device and test its reliability and dosimetric capabilities.
Approach: A 12-leaflet motorized iris collimator was designed...
Improve Deep Learning-Based Reconstruction of Optical Coherence Tomography Angiography by Siamese U-Net [0.03%]
基于Siamese U-Net的光学相干断层扫描血管成像的深度学习重建方法研究
Kewei Zhang,Zhilong Yan,Xinyuan Cao et al.
Kewei Zhang et al.
Optical coherence tomography angiography (OCTA), as a functional imaging based on OCT, has found successful medical applications. OCTA produces vasculature imaging using blood flow motion as an intrinsic contrast agent. To date, the prevail...
A Monte Carlo dose engine for fast neutron therapy and boron neutron capture therapy for matRad [0.03%]
一种用于快中子治疗和硼中子俘获治疗的Monte-Carlo剂量引擎用于matRad
Lucas Sommer,Tobias Chemnitz,Niklas Wahl et al.
Lucas Sommer et al.
Objective:The purpose of the work presented here was to enable easy access to Monte Carlo dose calculation for both fast neutron therapy and boron neutron capture therapy for research purposes. The dose calculation approach was especially i...
Classification of cardiac electrical signals between patients with myocardial infarction and healthy controls by using time-frequency features and 3D convolutional neural networks [0.03%]
基于时频特征和三维卷积神经网络的心肌梗死患者与健康人的心电图分类
Muqing Deng,Boyan Li,Mingying Ma et al.
Muqing Deng et al.
Electrocardiogram (ECG) signal classification plays an important role in myocardial infarction (MI) detection and screening. Despite that much progress has been made, the interpretation of ECG signals is still extremely time-consuming, and ...