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Related Concept Videos

Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

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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...
276
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

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Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

249
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.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
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Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

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Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
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One-Compartment Open Model: Urinary Excretion Data and Determination of k01:11

One-Compartment Open Model: Urinary Excretion Data and Determination of k

629
The one-compartment open model leverages urinary excretion data to estimate renal clearance, which gauges the kidney's capacity to expel a drug. This method offers several benefits, including directly measuring drug elimination and assessing the kidney's contribution to overall drug clearance. However, this approach has limitations. It assumes sole renal excretion of the drug, which is not true for all drugs. Accurate urinary excretion and plasma drug concentration measurement can also...
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How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

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A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
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Related Experiment Video

Updated: Feb 3, 2026

High-resolution Confocal Imaging of the Blood-brain Barrier: Imaging, 3D Reconstruction, and Quantification of Transcytosis
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Rapid T1 quantification from high resolution 3D data with model-based reconstruction.

Oliver Maier1,2, Jasper Schoormans3, Matthias Schloegl1,2

  • 1Institute of Medical Engineering, Graz University of Technology, Graz, Austria.

Magnetic Resonance in Medicine
|October 23, 2018
PubMed
Summary
This summary is machine-generated.

Accelerated magnetic resonance imaging (MRI) protocols significantly reduce scan times for quantitative T1 mapping. This new model-based reconstruction method achieves high-resolution 3D T1 maps from undersampled data, improving clinical applicability.

Keywords:
MRIT1 quantificationconstrained reconstructionimaginginversion-recovery Look-Lockermodel-based reconstructionvariable flip angle

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Area of Science:

  • Medical Imaging
  • Biophysics
  • Computational Science

Background:

  • Quantitative magnetic resonance imaging (MRI) protocols often require long acquisition times due to multiple measurements.
  • Accelerating MRI acquisition is crucial for enhancing clinical applicability and patient throughput.
  • Current methods for generating high-resolution 3D T1 maps are time-consuming.

Purpose of the Study:

  • To develop and validate an accelerated 3D radial MRI acquisition and reconstruction framework for quantitative T1 mapping.
  • To improve the efficiency of generating high-resolution 3D T1 maps without compromising accuracy.
  • To enable faster clinical assessments using quantitative MRI data.

Main Methods:

  • A model-based optimization framework was developed for reconstructing undersampled 3D radial stack-of-stars data.
  • Iteratively regularized Gauss-Newton method was employed to solve the non-linear, non-differentiable optimization problem.
  • 3D-spectral regularization was utilized, coupling spatial and parametric information for enhanced reconstruction, validated with variable flip angle (VFA) and inversion recovery Look-Locker (IRLL) methods.

Main Results:

  • The proposed method successfully generated high-resolution 3D T1 maps from highly undersampled data.
  • Numerical simulations and phantom scans demonstrated excellent quantitative agreement and image quality.
  • In vivo T1 maps from accelerated 3D acquisitions showed high accordance with fully sampled reference reconstructions.

Conclusions:

  • The developed algorithm enables the recovery of 1 mm³ isotropic resolution T1 maps from highly undersampled radial MRI data.
  • Exploiting structural similarities across spatial and parametric dimensions significantly improves reconstruction quality.
  • This acceleration technique enhances the clinical feasibility of quantitative 3D T1 mapping.