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 Experiment Video

Updated: Mar 27, 2026

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function
02:09

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Published on: April 12, 2024

1.2K

EIT image regularization by a new Multi-Objective Simulated Annealing algorithm.

Thiago Castro Martins, Marcos Sales Guerra Tsuzuki

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |January 7, 2016
    PubMed
    Summary
    This summary is machine-generated.

    Related Concept Videos

    Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

    Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

    397
    Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
    In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
    397
    One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

    One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

    1.4K
    This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
    On...
    1.4K

    You might also read

    Related Articles

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

    Sort by
    Same author

    Sinusoidal Representation of the Electrical Impedance Tomography Inverse Problem.

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
    Same author

    Self-compensation methodology for ultrasonic thickness gauges.

    Ultrasonics·2023
    See all related articles

    This study introduces a new Simulated Annealing-based Multi-Objective Optimization algorithm for Electrical Impedance Tomography (EIT) image reconstruction. The novel method enhances image quality compared to traditional single and multi-objective techniques.

    Area of Science:

    • Medical Imaging
    • Computational Science

    Background:

    • Electrical Impedance Tomography (EIT) image reconstruction often requires regularization, but the optimal weight is unknown.
    • Existing single and multi-objective optimization methods have limitations in EIT image reconstruction.

    Purpose of the Study:

    • To propose a novel Multi-Objective Optimization (MOO) algorithm for regularized EIT image reconstruction.
    • To address the challenge of unknown regularization term weights in EIT.

    Main Methods:

    • Developed a novel MOO algorithm using Simulated Annealing specifically for EIT.
    • Reconstructed EIT images from experimental data using the proposed algorithm.
    • Compared the performance against traditional single and multi-objective optimization methods.

    More Related Videos

    Surrogate Model Development for Digital Experiments in Welding
    09:17

    Surrogate Model Development for Digital Experiments in Welding

    Published on: March 28, 2025

    2.1K
    Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
    11:53

    Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

    Published on: December 9, 2012

    13.6K

    Related Experiment Videos

    Last Updated: Mar 27, 2026

    Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function
    02:09

    Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

    Published on: April 12, 2024

    1.2K
    Surrogate Model Development for Digital Experiments in Welding
    09:17

    Surrogate Model Development for Digital Experiments in Welding

    Published on: March 28, 2025

    2.1K
    Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
    11:53

    Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

    Published on: December 9, 2012

    13.6K

    Main Results:

    • The proposed Simulated Annealing-based MOO algorithm demonstrated superior performance in EIT image reconstruction.
    • Quantitative and qualitative improvements were observed compared to existing techniques.
    • The algorithm effectively handles regularization without prior weight knowledge.

    Conclusions:

    • The novel MOO algorithm offers a significant advancement for EIT image reconstruction.
    • Simulated Annealing is a viable approach for optimizing EIT regularization.
    • This method provides enhanced performance and robustness for EIT applications.