scholarly journals A Patient-Specific 3D+t Coronary Artery Motion Modeling Method Using Hierarchical Deformation with Electrocardiogram

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5680 ◽  
Author(s):  
Siyeop Yoon ◽  
Changhwan Yoon ◽  
Eun Ju Chun ◽  
Deukhee Lee
Keyword(s):  
Coronary Artery ◽  
Real Time ◽  
Average Distance ◽  
Vital Role ◽  
Patient Specific ◽  
Ecg Signal ◽  
Motion Modeling ◽  
Shape Information ◽  
Deformation Control ◽  
S Model

Cardiovascular-related diseases are one of the leading causes of death worldwide. An understanding of heart movement based on images plays a vital role in assisting postoperative procedures and processes. In particular, if shape information can be provided in real-time using electrocardiogram (ECG) signal information, the corresponding heart movement information can be used for cardiovascular analysis and imaging guides during surgery. In this paper, we propose a 3D+t cardiac coronary artery model which is rendered in real-time, according to the ECG signal, where hierarchical cage-based deformation modeling is used to generate the mesh deformation used during the procedure. We match the blood vessel’s lumen obtained from the ECG-gated 3D+t CT angiography taken at multiple cardiac phases, in order to derive the optimal deformation. Splines for 3D deformation control points are used to continuously represent the obtained deformation in the multi-view, according to the ECG signal. To verify the proposed method, we compare the manually segmented lumen and the results of the proposed method for eight patients. The average distance and dice coefficient between the two models were 0.543 mm and 0.735, respectively. The required time for registration of the 3D coronary artery model was 23.53 s/model. The rendering speed to derive the model, after generating the 3D+t model, was faster than 120 FPS.

scholarly journals A Patient-Specific 3D+t Coronary Artery Motion Modeling Method Using a Hierarchical Deformation with Electrocardiogram.

2020 ◽  
Author(s):  
Siyeop Yoon ◽  
Changhwan Yoon ◽  
Eun ju Chun ◽  
Deukhee Lee
Keyword(s):  
Coronary Artery ◽  
Real Time ◽  
Average Distance ◽  
Vital Role ◽  
Patient Specific ◽  
Ecg Signal ◽  
Motion Modeling ◽  
Shape Information ◽  
Deformation Control ◽  
Coronary Artery Motion

Cardiovascular-related diseases are one of the leading causes of death worldwide. An understanding of heart movement based on images plays a vital role in assisting the procedure in the postoperative and postoperative processes. In particular, if the shape information can be provided in real-time using the electrocardiogram(ECG) signal using this information, the heart’s movement information can be used for cardiovascular analysis and imaging guides during surgery. In this paper, we propose creating a 3D+t cardiac coronary artery model that is rendered in real-time according to the ECG signal. Hierarchical cage-based deformation modeling is used to generate mesh deformation used during the procedure according to the ECG signal. We match the blood vessel’s lumen obtained from the ECG-gated 3D+t CT angiography taken at the multiple cardiac phases to derive the optimal deformation. Splines for 3D deformation control points were used to continuously represent the obtained deformation at the multi-view according to the ECG signal. To verify the proposed method, we compared the manually segmented lumen and results of the proposed method for eight patients. The average distance and dice coefficient between the two models was 0.543mm and 0.735, respectively. The required time for registration of the 3D coronary artery model is 23.53 seconds/model. rendering speed to derive the model according to the ECG signal after generating the 3D+t model is faster than 120 FPS.

Real Time Gender and Age Prediction using Deep Learning Techniques

2020 ◽  
Vol 9 (6) ◽  
pp. 797-801
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Real Time ◽  
Vital Role ◽  
Learning Techniques ◽  
Gender And Age ◽  
The Face ◽  
And Gender ◽  
Gender Detection ◽  
Age Prediction

Face recognition plays a vital role in security purpose. In recent years, the researchers have focused on the pose illumination, face recognition, etc,. The traditional methods of face recognition focus on Open CV’s fisher faces which results in analyzing the face expressions and attributes. Deep learning method used in this proposed system is Convolutional Neural Network (CNN). Proposed work includes the following modules: [1] Face Detection [2] Gender Recognition [3] Age Prediction. Thus the results obtained from this work prove that real time age and gender detection using CNN provides better accuracy results compared to other existing approaches.

Understanding the role of Inflammasome in Angina Pectoris

2020 ◽  
Vol 21 ◽  
Author(s):  
Ishita Sharma ◽  
Tapan Behl ◽  
Simona Bungau ◽  
Monika Sachdeva ◽  
Arun Kumar ◽  
...  
Keyword(s):  
Coronary Artery ◽  
Angina Pectoris ◽  
Nlrp3 Inflammasome ◽  
Vital Role ◽  
Death Process ◽  
Cell Death Process ◽  
Artery Disease ◽  
Species Production ◽  
Pro Inflammatory Cytokines

Abstract:: Angina pectoris, associated with coronary artery disease, a cardiovascular disease where, pain is caused by adverse oxygen supply in myocardium, resulting in contractility and discomfort in chest. Inflammasomes, triggered by stimuli due to infection and cellular stress have identified to play a vital role in the progression of cardiovascular disorders and thus, causing various symptoms like angina pectoris. Nlrp3 inflammasome, a key contributor in the pathogenesis of angina pectoris, requires activation and primary signaling for the commencement of inflammation. Nlrp3 inflammasome elicit out an inflammatory response by emission of pro inflammatory cytokines by ROS (reactive oxygen species) production, mobilization of K+ efflux and Ca2+ and by activation of lysosome destabilization that eventually causes pyroptosis, a programmed cell death process. Thus, inflammasome are considered to be one of the factors involved in the progression of coronary artery diseases and have an intricate role in development of angina pectoris.

scholarly journals Fluid–structure interactions (FSI) based study of low-density lipoproteins (LDL) uptake in the left coronary artery

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xueping Chen ◽  
Jian Zhuang ◽  
Huanlei Huang ◽  
Yueheng Wu
Keyword(s):  
Shear Stress ◽  
Coronary Artery ◽  
Left Coronary Artery ◽  
Arterial Wall ◽  
Low Density Lipoproteins ◽  
Patient Specific ◽  
Low Density ◽  
Fluid Structure ◽  
Flowing Blood ◽  
Stress Dependent

AbstractThe purpose of this study is to compare the effect of the different physical factors on low-density lipoproteins (LDL) accumulation from flowing blood to the arterial wall of the left coronary arteries. The three-dimensional (3D) computational model of the left coronary arterial tree is reconstructed from a patient-specific computed tomography angiography (CTA) image. The endothelium of the coronary artery is represented by a shear stress dependent three-pore model. Fluid–structure interaction ($$FSI$$ FSI ) based numerical method is used to study the LDL transport from vascular lumen into the arterial wall. The results show that the high elastic property of the arterial wall decreases the complexity of the local flow field in the coronary bifurcation system. The places of high levels of LDL uptake coincide with the regions of low wall shear stress. In addition, hypertension promotes LDL uptake from flowing blood in the arterial wall, while the thickened arterial wall decreases this process. The present computer strategy combining the methods of coronary CTA image 3D reconstruction, $$FSI$$ FSI simulation, and three-pore modeling was illustrated to be effective on the simulation of the distribution and the uptake of LDL. This may have great potential for the early prediction of the local atherosclerosis lesion in the human left coronary artery.

RSNet: Rail semantic segmentation network for extracting aerial railroad images

2021 ◽  
pp. 1-18
Author(s):  
R.S. Rampriya ◽  
Sabarinathan ◽  
R. Suganya
Keyword(s):  
Real Time ◽  
Visual Processing ◽  
Feature Fusion ◽  
Semantic Segmentation ◽  
Vital Role ◽  
Obstacle Detection ◽  
Aerial Images ◽  
Computationally Efficient ◽  
Fusion Algorithm ◽  
Uav Images

In the near future, combo of UAV (Unmanned Aerial Vehicle) and computer vision will play a vital role in monitoring the condition of the railroad periodically to ensure passenger safety. The most significant module involved in railroad visual processing is obstacle detection, in which caution is obstacle fallen near track gage inside or outside. This leads to the importance of detecting and segment the railroad as three key regions, such as gage inside, rails, and background. Traditional railroad segmentation methods depend on either manual feature selection or expensive dedicated devices such as Lidar, which is typically less reliable in railroad semantic segmentation. Also, cameras mounted on moving vehicles like a drone can produce high-resolution images, so segmenting precise pixel information from those aerial images has been challenging due to the railroad surroundings chaos. RSNet is a multi-level feature fusion algorithm for segmenting railroad aerial images captured by UAV and proposes an attention-based efficient convolutional encoder for feature extraction, which is robust and computationally efficient and modified residual decoder for segmentation which considers only essential features and produces less overhead with higher performance even in real-time railroad drone imagery. The network is trained and tested on a railroad scenic view segmentation dataset (RSSD), which we have built from real-time UAV images and achieves 0.973 dice coefficient and 0.94 jaccard on test data that exhibits better results compared to the existing approaches like a residual unit and residual squeeze net.

scholarly journals Evaluating a calcium-aware kernel for CT CAC scoring with varying surrounding materials and heart rates: a dynamic phantom study

2021 ◽  
Author(s):  
Niels R. van der Werf ◽  
Ronald Booij ◽  
Bernhard Schmidt ◽  
Thomas G. Flohr ◽  
Tim Leiner ◽  
...  
Keyword(s):  
Coronary Artery ◽  
Soft Tissue ◽  
Patient Specific ◽  
Reconstruction Technique ◽  
Ct Reconstruction ◽  
Raw Data ◽  
Heart Rates ◽  
Tissue Equivalent ◽  
Tin Filtration ◽  
Reconstruction Kernel

Abstract Objectives The purpose of this study was twofold. First, the influence of a novel calcium-aware (Ca-aware) computed tomography (CT) reconstruction technique on coronary artery calcium (CAC) scores surrounded by a variety of tissues was assessed. Second, the performance of the Ca-aware reconstruction technique on moving CAC was evaluated with a dynamic phantom. Methods An artificial coronary artery, containing two CAC of equal size and different densities (196 ± 3, 380 ± 2 mg hydroxyapatite cm−3), was moved in the center compartment of an anthropomorphic thorax phantom at different heart rates. The center compartment was filled with mixtures, which resembled fat, water, and soft tissue equivalent CT numbers. Raw data was acquired with a routine clinical CAC protocol, at 120 peak kilovolt (kVp). Subsequently, reduced tube voltage (100 kVp) and tin-filtration (150Sn kVp) acquisitions were performed. Raw data was reconstructed with a standard and a novel Ca-aware reconstruction technique. Agatston scores of all reconstructions were compared with the reference (120 kVp) and standard reconstruction technique, with relevant deviations defined as > 10%. Results For all heart rates, Agatston scores for CAC submerged in fat were comparable to the reference, for the reduced-kVp acquisition with Ca-aware reconstruction kernel. For water and soft tissue, medium-density Agatston scores were again comparable to the reference for all heart rates. Low-density Agatston scores showed relevant deviations, up to 15% and 23% for water and soft tissue, respectively. Conclusion CT CAC scoring with varying surrounding materials and heart rates is feasible at patient-specific tube voltages with the novel Ca-aware reconstruction technique. Key Points • A dedicated calcium-aware reconstruction kernel results in similar Agatston scores for CAC surrounded by fatty materials regardless of CAC density and heart rate. • Application of a dedicated calcium-aware reconstruction kernel allows for radiation dose reduction. • Mass scores determined with CT underestimated physical mass.

scholarly journals NOAA Satellite Soil Moisture Operational Product System (SMOPS) Version 3.0 Generates Higher Accuracy Blended Satellite Soil Moisture

2020 ◽  
Vol 12 (17) ◽  
pp. 2861
Author(s):  
Jifu Yin ◽  
Xiwu Zhan ◽  
Jicheng Liu
Keyword(s):  
Soil Moisture ◽  
Real Time ◽  
Land Surface ◽  
Vital Role ◽  
Product System ◽  
Global Soil ◽  
One Stop ◽  
Satellite Sensors ◽  
Blended Data

Soil moisture plays a vital role for the understanding of hydrological, meteorological, and climatological land surface processes. To meet the need of real time global soil moisture datasets, a Soil Moisture Operational Product System (SMOPS) has been developed at National Oceanic and Atmospheric Administration to produce a one-stop shop for soil moisture observations from all available satellite sensors. What makes the SMOPS unique is its near real time global blended soil moisture product. Since the first version SMOPS publicly released in 2010, the SMOPS has been updated twice based on the users’ feedbacks through improving retrieval algorithms and including observations from new satellite sensors. The version 3.0 SMOPS has been operationally released since 2017. Significant differences in climatological averages lead to remarkable distinctions in data quality between the newest and the older versions of SMOPS blended soil moisture products. This study reveals that the SMOPS version 3.0 has overwhelming advantages of reduced data uncertainties and increased correlations with respect to the quality controlled in situ measurements. The new version SMOPS also presents more robust agreements with the European Space Agency’s Climate Change Initiative (ESA_CCI) soil moisture datasets. With the higher accuracy, the blended data product from the new version SMOPS is expected to benefit the hydrological, meteorological, and climatological researches, as well as numerical weather, climate, and water prediction operations.

GPU-Based Haptic Simulator for Dental Bone Drilling

2011 ◽  
Author(s):  
Fei Zheng ◽  
WenFeng Lu ◽  
Yoke San Wong ◽  
Kelvin Weng Chiong Foong
Keyword(s):  
Real Time ◽  
Graphics Processing Units ◽  
Force Feedback ◽  
Mandibular Canal ◽  
Structure Design ◽  
Patient Specific ◽  
Force Model ◽  
Bone Drilling ◽  
The Real ◽  
Drill Bits

Dental bone drilling is an inexact and often a blind art. Dentist risks damaging the invisible tooth roots, nerves and critical dental structures like mandibular canal and maxillary sinus. This paper presents a haptics-based jawbone drilling simulator for novice surgeons. Through the real-time training of tactile sensations based on patient-specific data, improved outcomes and faster procedures can be provided. Previously developed drilling simulators usually adopt penalty-based contact force models and often consider only spherical-shaped drill bits for simplicity and computational efficiency. In contrast, our simulator is equipped with a more precise force model, adapted from the Voxmap-PointShell (VPS) method to capture the essential features of the drilling procedure. In addition, the proposed force model can accommodate various shapes of drill bits. To achieve better anatomical accuracy, our oral model has been reconstructed from Cone Beam CT, using voxel-based method. To enhance the real-time response, the parallel computing power of Graphics Processing Units is exploited through extra efforts for data structure design, algorithms parallelization, and graphic memory utilization. Preliminary results show that the developed system can produce appropriate force feedback at different tissue layers.

Sign in / Sign up

Export Citation Format

Share Document