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Related Concept Videos

Variability: Analysis01:11

Variability: Analysis

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Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
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A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
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An equation with two variables, typically written in the form y = f(x) or Ax + By = C, describes a relationship between quantities represented by x and y. Each solution to such an equation is an ordered pair (x, y) that satisfies the equation when substituted. These pairs can be represented graphically to understand the variables' relationship visually.A common technique for constructing the graph of a two-variable equation is to create a value table. Begin by choosing several values for the...
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Fluorescence and phosphorescence are essential phenomena in fields like analytical chemistry, biological imaging, and materials science, where they detect molecular properties and visualize cellular structures. Understanding the variables that influence these luminescent behaviors is crucial for maximizing accuracy and efficiency in their applications. These variables can broadly be grouped into chemical structure, solvent properties, and external conditions, each playing a distinct role in...
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Ideal Solutions02:24

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According to Raoult’s law, the partial vapor pressure of a solvent in a solution is equal or identical to the vapor pressure of the pure solvent multiplied by its mole fraction in the solution. However, Raoult's Law is only valid for ideal solutions. For a solution to be ideal, the solvent-solute interaction must be just as strong as a solvent-solvent or solute-solute interaction. This suggests that both the solute and the solvent would use the same amount of energy to escape to the...
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Many common substances around us exist as a solution, such as ocean water, air, and gasoline. All solutions are mixtures of substances that are composed of varying amounts of two or more types of atoms or molecules. A mixture with a non-uniform composition is a heterogeneous mixture, whereas a mixture with a uniform composition is a homogeneous mixture. The components that make the homogeneous mixture are evenly spread out and thoroughly mixed. 
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Quantitative [18F]-Naf-PET-MRI Analysis for the Evaluation of Dynamic Bone Turnover in a Patient with Facetogenic Low Back Pain
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Variability in Quantitative DCE-MRI: Sources and Solutions.

Harrison Kim1

  • 1Department of Radiology, University of Alabama at Birmingham, VH G082C5, 1670 University Boulevard, Birmingham, AL 35294-0012 (phone: 205-996-4088, fax: 205-975-6522.

Journal of Nature and Science
|March 13, 2018
PubMed
Summary
This summary is machine-generated.

Quantitative dynamic contrast-enhanced MRI (DCE-MRI) perfusion parameters vary significantly across sites. This review discusses sources of variability and solutions for reliable multi-institutional clinical trials.

Keywords:
DCE-MRIPhantomPharmacokinetic analysisQuality assuranceQuantification

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Area of Science:

  • Medical Imaging
  • Radiology
  • Oncology

Background:

  • Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) is crucial for cancer diagnosis, prognosis, and therapy monitoring.
  • Significant variability in perfusion parameters measured by DCE-MRI hinders multi-institutional clinical trial data comparison.
  • Existing perfusion phantoms address scanner-driven errors but not all sources of variability.

Purpose of the Study:

  • To review the diverse sources influencing quantitative DCE-MRI measurement variability.
  • To discuss strategies for minimizing these variations in multi-institutional settings.

Main Methods:

  • Literature review of factors affecting quantitative DCE-MRI measurements.
  • Analysis of scanner, software, and protocol-related variability.

Main Results:

  • Variability stems from MRI scanners, software packages, and operator-defined imaging protocols.
  • Novel perfusion phantoms can correct scanner-specific errors.

Conclusions:

  • Standardizing software and imaging protocols is essential alongside phantom use.
  • Minimizing variability is critical for robust multi-institutional DCE-MRI studies and reliable clinical decision-making.