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Related Concept Videos

Diffusion01:12

Diffusion

Diffusion is the passive movement of substances down their concentration gradients—requiring no expenditure of cellular energy. Substances, such as molecules or ions, diffuse from an area of high concentration to an area of low concentration in the cytosol or across membranes. Eventually, the concentration will even out, with the substance moving randomly but causing no net change in concentration. Such a state is called dynamic equilibrium, which is essential for maintaining overall...
Assessment of Diffusion and Perfusion01:17

Assessment of Diffusion and Perfusion

Understanding and evaluating diffusion and perfusion is critical in assessing a patient's respiratory and circulatory health. These processes play key roles in maintaining the body's internal environment, ensuring that tissues receive adequate oxygen while waste products are efficiently removed.
The Role of Diffusion in Respiration
Diffusion is the process by which molecules move from an area of higher concentration to an area of lower concentration. In the respiratory system, this principle...
¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)01:20

¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)

When proton-coupled carbon-13 spectra are simplified by a broadband proton decoupling technique, structural information about the coupled protons is lost. Distortionless enhancement by polarization transfer (DEPT) is a technique that provides information on the number of hydrogens attached to each carbon in a molecule. While the DEPT experiment utilizes complex pulse sequences, the pulse delay and flip angle are specifically manipulated. The resulting signals have different phases depending on...
¹H NMR: Interpreting Distorted and Overlapping Signals01:02

¹H NMR: Interpreting Distorted and Overlapping Signals

Spin systems where the difference in chemical shifts of the coupled nuclei is greater than ten times J are called first-order spin systems. These nuclei are weakly coupled, and their chemical shifts and coupling constant can generally be estimated from the well-separated signals in the spectrum.
As Δν decreases and the signals move closer, the doublets appear increasingly distorted. The intensities of the inner lines increase at the cost of those of the outer lines as the signals are slanted or...
Extraction: Partition and Distribution Coefficients01:14

Extraction: Partition and Distribution Coefficients

The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
For extracting a solute from an aqueous phase into an organic...

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Related Experiment Video

Updated: Jun 21, 2026

Image Processing Protocol for the Analysis of the Diffusion and Cluster Size of Membrane Receptors by Fluorescence Microscopy
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Efficient and robust computation of PDF features from diffusion MR signal.

Haz-Edine Assemlal1, David Tschumperlé, Luc Brun

  • 1GREYC (CNRS UMR 6072), 6 Bd Maréchal Juin, 14050 Caen Cedex, France. assemlal@greyc.ensicaen.fr

Medical Image Analysis
|August 12, 2009
PubMed
Summary

This study introduces a novel diffusion magnetic resonance imaging method to estimate tissue micro-architecture features. The technique enhances robustness against Rician noise, providing detailed insights into local tissue structure.

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Last Updated: Jun 21, 2026

Image Processing Protocol for the Analysis of the Diffusion and Cluster Size of Membrane Receptors by Fluorescence Microscopy
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Area of Science:

  • Biomedical Engineering
  • Medical Imaging
  • Computational Biology

Background:

  • Diffusion Magnetic Resonance Imaging (dMRI) is crucial for non-invasively probing tissue microstructure.
  • Estimating micro-architectural features from dMRI signals is challenging due to noise and complex signal behavior.
  • Existing methods may lack robustness or flexibility in feature extraction.

Purpose of the Study:

  • To develop a robust and flexible method for estimating tissue micro-architectural features from dMRI.
  • To address the challenge of Rician noise in in-vivo dMRI data.
  • To enable the computation of a wide range of local tissue structure features.

Main Methods:

  • Signal approximation using series expansion of Gaussian-Laguerre and Spherical Harmonics functions.
  • Projection onto a finite dimensional space for feature estimation.
  • A variational minimization framework to ensure robustness against Rician noise.

Main Results:

  • The proposed method effectively estimates various tissue micro-architecture features.
  • Demonstrated robustness to Rician noise in simulated and real dMRI data.
  • The approach is flexible with varying numbers of samples, enabling diverse feature computation.

Conclusions:

  • The developed dMRI method provides a robust and flexible tool for tissue micro-architecture analysis.
  • This technique enhances the reliability of feature estimation from noisy in-vivo data.
  • The method facilitates comprehensive characterization of local tissue structures.