Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Application of a Generative Adversarial Network in Image Reconstruction of Magnetic Induction Tomography.

Sensors (Basel, Switzerland)·2021
Same author

A Multi-Scale Feature Fusion Method Based on U-Net for Retinal Vessel Segmentation.

Entropy (Basel, Switzerland)·2020
Same author

Application of Linear Gradient Magnetic Field in Arterial Profile Scanning Imaging.

Sensors (Basel, Switzerland)·2020
Same author

A Blood Flow Volume Linear Inversion Model Based on Electromagnetic Sensor for Predicting the Rate of Arterial Stenosis.

Sensors (Basel, Switzerland)·2019
Same author

Solution-solid-solid mechanism: superionic conductors catalyze nanowire growth.

Nano letters·2013
Same author

Depending on the stage of hepatosteatosis, p53 causes apoptosis primarily through either DRAM-induced autophagy or BAX.

Liver international : official journal of the International Association for the Study of the Liver·2013

Related Experiment Video

Updated: Oct 18, 2025

Assessing Cerebral Autoregulation via Oscillatory Lower Body Negative Pressure and Projection Pursuit Regression
11:26

Assessing Cerebral Autoregulation via Oscillatory Lower Body Negative Pressure and Projection Pursuit Regression

Published on: December 10, 2014

12.5K

A Deep Neural Network Method for Arterial Blood Flow Profile Reconstruction.

Dan Yang1,2,3, Yuchen Wang1,2, Bin Xu4

  • 1School of Information Science & Engineering, Northeastern University, Shenyang 110819, China.

Entropy (Basel, Switzerland)
|September 28, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a deep neural network method for reconstructing arterial blood flow profiles, improving early detection of arterial stenosis. The novel approach offers high accuracy for cardiovascular disease monitoring.

Keywords:
arterial blood flow profile reconstructionartery stenosisdeep neural networkelectromagnetic effect

More Related Videos

Cerebral Blood Flow-Based Resting State Functional Connectivity of the Human Brain using Optical Diffuse Correlation Spectroscopy
07:13

Cerebral Blood Flow-Based Resting State Functional Connectivity of the Human Brain using Optical Diffuse Correlation Spectroscopy

Published on: May 27, 2020

6.7K
Author Spotlight: Noninvasive Cerebral Blood Flow Determination in Human Functional Brain Region for Diagnosis of Neurological Disorders
05:23

Author Spotlight: Noninvasive Cerebral Blood Flow Determination in Human Functional Brain Region for Diagnosis of Neurological Disorders

Published on: May 31, 2024

679

Related Experiment Videos

Last Updated: Oct 18, 2025

Assessing Cerebral Autoregulation via Oscillatory Lower Body Negative Pressure and Projection Pursuit Regression
11:26

Assessing Cerebral Autoregulation via Oscillatory Lower Body Negative Pressure and Projection Pursuit Regression

Published on: December 10, 2014

12.5K
Cerebral Blood Flow-Based Resting State Functional Connectivity of the Human Brain using Optical Diffuse Correlation Spectroscopy
07:13

Cerebral Blood Flow-Based Resting State Functional Connectivity of the Human Brain using Optical Diffuse Correlation Spectroscopy

Published on: May 27, 2020

6.7K
Author Spotlight: Noninvasive Cerebral Blood Flow Determination in Human Functional Brain Region for Diagnosis of Neurological Disorders
05:23

Author Spotlight: Noninvasive Cerebral Blood Flow Determination in Human Functional Brain Region for Diagnosis of Neurological Disorders

Published on: May 31, 2024

679

Area of Science:

  • Biomedical Engineering
  • Medical Imaging
  • Computational Fluid Dynamics

Background:

  • Arterial stenosis reduces blood flow, leading to cardiovascular diseases.
  • Current diagnostic methods are costly and complex, hindering early prediction.
  • Electromagnetic effects of arterial blood flow offer a potential diagnostic avenue.

Purpose of the Study:

  • To develop a deep neural network-based method for accurate arterial blood flow profile reconstruction.
  • To enable early prediction and monitoring of arterial stenosis and related cardiovascular diseases.
  • To overcome limitations of existing diagnostic techniques in terms of cost and complexity.

Main Methods:

  • Utilized a deep neural network, incorporating a convolutional auto-encoder (CAE) and a convolutional neural network (CNN).
  • Input data included potential difference and weight matrix derived from arterial blood flow models.
  • Simulations performed using COMSOL on carotid artery models with varying stenosis rates in a uniform magnetic field.

Main Results:

  • Achieved an average root mean square error of 0.0333 and an average correlation coefficient of 0.9721.
  • Demonstrated superior performance compared to Tikhonov, back propagation (BP), and standard CNN methods.
  • High accuracy in reconstructing blood flow velocity distribution was confirmed through simulation.

Conclusions:

  • The proposed deep neural network method accurately reconstructs arterial blood flow profiles.
  • This technique holds significant potential for the early diagnosis of arterial stenosis.
  • Offers a promising, high-accuracy, and potentially cost-effective approach for cardiovascular disease monitoring.