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Related Concept Videos

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
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Model Approaches for Pharmacokinetic Data: Physiological Models01:15

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Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

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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.
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Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models00:57

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Physiological pharmacokinetic models, often called flow-limited or perfusion models, typically assume a swift drug distribution between tissue and venous blood, creating a rapid drug equilibrium. This premise is based on the idea that drug diffusion is extremely fast, and the cell membrane presents no barrier to drug permeation. In this scenario, where no drug binding occurs, the drug concentration in the tissue equals that of the venous blood leaving the tissue. This greatly simplifies the...
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Pharmacokinetic Models: Comparison and Selection Criterion01:26

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Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
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One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

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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.
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Maximum likelihood-based extended Kalman filter for soft tissue modelling.

Jialu Song1, Hujin Xie1, Yongmin Zhong1

  • 1School of Engineering, RMIT University, Australia.

Journal of the Mechanical Behavior of Biomedical Materials
|November 14, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for realistic soft tissue modeling by dynamically identifying mechanical properties. This approach improves accuracy and computational efficiency in medical simulations.

Keywords:
And maximum likelihood theoryDeformable tissue modellingExtended Kalman filterNonlinear finite element methodTissue mechanical properties

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Area of Science:

  • Biomedical Engineering
  • Computational Mechanics
  • Medical Simulation

Background:

  • Accurate modeling of human soft tissue is crucial for medical applications.
  • Existing nonlinear finite element methods (NFEM) face computational cost limitations.
  • Dynamic characterization of tissue mechanical properties remains a challenge.

Purpose of the Study:

  • To develop a novel method for enhanced soft tissue modeling fidelity.
  • To dynamically identify unknown mechanical properties of soft tissues during deformation.
  • To improve the accuracy and computational efficiency of soft tissue deformation estimation.

Main Methods:

  • Formulating nonlinear tissue deformation as a nonlinear filtering identification problem.
  • Combining maximum likelihood theory, nonlinear filtering, and nonlinear finite element method (NFEM).
  • Developing a maximum likelihood-based extended Kalman filter for dynamic property identification and deformation estimation.

Main Results:

  • The proposed method dynamically identifies tissue mechanical properties during deformation.
  • It achieves high accuracy in modeling homogeneous tissue deformation, similar to NFEM.
  • The method overcomes the computational expense limitations associated with traditional NFEM.

Conclusions:

  • The novel method offers a computationally efficient and accurate approach to soft tissue modeling.
  • Dynamic identification of mechanical properties enhances the fidelity of medical simulations.
  • This technique holds significant potential for improving diagnostic and surgical planning tools.