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Related Concept Videos

Three-Compartment Open Model01:06

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The three-compartment open model is a pharmacokinetic model used to describe the distribution and elimination of drugs following extravascular administration. It comprises a central compartment representing the plasma and two peripheral compartments. The highly perfused peripheral compartment represents organs and tissues with a rich blood supply, such as the liver, kidneys, and lungs. The scarcely perfused peripheral compartment represents tissues with lower blood supply, such as adipose...
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Compartment Models: Two-Compartment Model01:20

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The two-compartment model divides the body into central and peripheral compartments to account for varying blood perfusion rates among organs and tissues, affecting drug distribution. The central compartment includes blood and highly perfused tissues with rapid drug distribution, while the peripheral compartment contains tissues with slower drug distribution. After a single IV bolus dose, the drug concentration is high in plasma and low in tissues. The drug distribution between compartments...
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Compartment Models: Single-Compartment Model01:14

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The single-compartment model serves as a simplified representation of the human body. This model assumes that the body functions as a single, well-mixed open compartment. When a drug is administered intravenously, it enters the body and quickly distributes uniformly. The drug then undergoes biotransformation and elimination, ultimately leaving the body. The volume of this compartment is referred to as the apparent volume of distribution into which the drug can uniformly distribute. In this...
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Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
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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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Related Experiment Video

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Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
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Degeneracy in model parameter estimation for multi-compartmental diffusion in neuronal tissue.

Ileana O Jelescu1, Jelle Veraart1, Els Fieremans1

  • 1Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA.

NMR in Biomedicine
|December 1, 2015
PubMed
Summary
This summary is machine-generated.

Diffusion MRI (dMRI) model fitting struggles with accuracy and precision due to multiple parameter solutions and flat objective functions. This hinders the development of reliable neuroimaging biomarkers from noninvasive scans.

Keywords:
biophysical mechanisms of MR diffusionhigh order diffusion MR methodsmicrostructuremodelingnormal brainparameter estimation

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Area of Science:

  • Neuroimaging
  • Biophysics
  • Medical Physics

Background:

  • Diffusion MRI (dMRI) models offer promise for noninvasive neuroimaging biomarkers by probing neuronal microstructure.
  • Multi-compartment models are widely used to interpret water diffusion in the brain, but parameter estimation remains challenging.
  • Current methods often fix model parameters, improving precision at the cost of accuracy.

Purpose of the Study:

  • To investigate the reasons for poor parameter estimation in multi-compartment dMRI models.
  • To analyze the impact of local minima and flat parameter spaces on fitting accuracy and precision.
  • To identify challenges in deriving reliable biophysical interpretations from dMRI data.

Main Methods:

  • Utilized a representative two-compartment dMRI model with over 60 measurement points.
  • Analyzed the objective function landscape in the parameter space to identify local minima and flat regions.
  • Evaluated parameter estimation performance under realistic signal-to-noise ratio conditions.

Main Results:

  • Demonstrated that fitting fails to accurately determine five model parameters even with extensive data.
  • Identified two distinct local minima in the parameter space, both yielding biophysically plausible results.
  • Showed that distinguishing between these minima and estimating parameters reliably is difficult at realistic signal-to-noise ratios due to flat objective function profiles ('pipes').

Conclusions:

  • The inherent properties of the dMRI fitting process, including multiple minima and flat parameter spaces, lead to significant bias and uncertainty.
  • Accurate biophysical interpretation of dMRI parameters is compromised by these estimation challenges.
  • Further research is needed to determine the true minimum and quantify parameter uncertainty for robust clinical biomarkers.