Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Echo01:06

Echo

1.0K
The human ear cannot distinguish between two sources of sound if they happen to reach within a specific time interval, typically 0.1 seconds apart. More than this, and they are perceived as separate sources.
Imagine the sound is reflected back to the ears. Assuming that the source is very close to the human, the difference between hearing the two sounds—the emitted sound and the reflected sound—may be more than the minimum time for perceiving distinct sounds. If this is the case,...
1.0K
Diffusion01:12

Diffusion

221.8K
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...
221.8K
Diffusion01:21

Diffusion

6.6K
Diffusion is a type of passive transport. In passive transport, a substance tends to move from an area of high concentration to an area of low concentration until the concentration is equal across the space. For example, take the diffusion of substances through the air. When someone opens a perfume bottle in a room filled with people, the perfume is at its highest concentration in the bottle and is at its lowest at the edges of the room. The perfume vapor will diffuse, or spread away, from the...
6.6K
Chronopharmacokinetics: Time-Dependent Pharmacokinetics01:20

Chronopharmacokinetics: Time-Dependent Pharmacokinetics

419
Chronopharmacokinetics studies the temporal change in drug absorption and elimination. These changes can be cyclical or non-cyclical. Cyclical changes occur over a regular interval, while non-cyclical changes occur over a longer, irregular period.
Time-dependent pharmacokinetics refers to non-cyclical changes in drug rate processes over a period of time. It can lead to nonlinear pharmacokinetics, where the relationship between drug concentration and time is not proportional. Non-cyclical...
419
The Integrated Rate Law: The Dependence of Concentration on Time02:39

The Integrated Rate Law: The Dependence of Concentration on Time

43.8K
While the differential rate law relates the rate and concentrations of reactants, a second form of rate law called the integrated rate law relates concentrations of reactants and time. Integrated rate laws can be used to determine the amount of reactant or product present after a period of time or to estimate the time required for a reaction to proceed to a certain extent. For example, an integrated rate law helps determine the length of time a radioactive material must be stored for its...
43.8K
Nonlinear Pharmacokinetics: Dependence of Elimination Half-Life and Dose Clearance01:23

Nonlinear Pharmacokinetics: Dependence of Elimination Half-Life and Dose Clearance

695
The elimination half-life and drug clearance of drugs following nonlinear kinetics can vary with dosage. The Michaelis-Menten parameters and drug concentration influence these factors. As the dose increases, the elimination half-life tends to lengthen, resulting in a reduction in clearance and a disproportionately larger area under the curve. The total clearance can be derived from the Michaelis-Menten equation for drugs following a one-compartment model.
A study on guinea pigs examined the...
695

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Ratio of Left Atrial and Ventricular Volume as New Marker of Atrial Cardiopathy and Stroke Risk.

Stroke·2026
Same author

A deep-learning framework reveals whole-body perturbations at cell level.

Nature·2026
Same author

Four Directions, One Solution: Enabling Rapid Diffusion Tensor MRI for Ultra-Low Field Using Deep Learning.

Magnetic resonance in medicine·2026
Same author

Brain tumor segmentation in Sub-Saharan Africa patient population: The BraTS-Africa challenge.

Neuro-oncology advances·2026
Same author

The Ischemic Stroke Lesion Segmentation Challenge (ISLES)'24 Dataset: A Multimodal Stroke Imaging Dataset with Hyperacute CT, Acute Postinterventional MRI, and 3-month Clinical Outcomes.

Radiology. Artificial intelligence·2026
Same author

Protocol of the randomized double blind sham controlled AddVNS study of transcutaneous vagus nerve stimulation mechanisms in depression.

Scientific reports·2026

Related Experiment Video

Updated: Feb 12, 2026

Ultrasound Tissue Characterization of Human Achilles Tendon by Stability Quantification of Echo Patterns
08:11

Ultrasound Tissue Characterization of Human Achilles Tendon by Stability Quantification of Echo Patterns

Published on: September 5, 2025

535

A diffusion model-free framework with echo time dependence for free-water elimination and brain tissue microstructure

Miguel Molina-Romero1,2, Pedro A Gómez1,2, Jonathan I Sperl2

  • 1Department of Computer Science, Technical University of Munich, Garching, Germany.

Magnetic Resonance in Medicine
|March 25, 2018
PubMed
Summary

This study introduces blind source separation (BSS) to analyze brain tissue microstructure, offering a new method to disentangle diffusion and relaxation information and eliminate free-water contamination.

Keywords:
MR relaxometryblind source separationbrain microstructurediffusion MRIfree-water eliminationnonnegative matrix factorization

More Related Videos

Advanced Diffusion Imaging in The Hippocampus of Rats with Mild Traumatic Brain Injury
10:33

Advanced Diffusion Imaging in The Hippocampus of Rats with Mild Traumatic Brain Injury

Published on: August 14, 2019

9.0K
Layer Microdissection of Tricuspid Valve Leaflets for Biaxial Mechanical Characterization and Microstructural Quantification
07:34

Layer Microdissection of Tricuspid Valve Leaflets for Biaxial Mechanical Characterization and Microstructural Quantification

Published on: February 10, 2022

2.4K

Related Experiment Videos

Last Updated: Feb 12, 2026

Ultrasound Tissue Characterization of Human Achilles Tendon by Stability Quantification of Echo Patterns
08:11

Ultrasound Tissue Characterization of Human Achilles Tendon by Stability Quantification of Echo Patterns

Published on: September 5, 2025

535
Advanced Diffusion Imaging in The Hippocampus of Rats with Mild Traumatic Brain Injury
10:33

Advanced Diffusion Imaging in The Hippocampus of Rats with Mild Traumatic Brain Injury

Published on: August 14, 2019

9.0K
Layer Microdissection of Tricuspid Valve Leaflets for Biaxial Mechanical Characterization and Microstructural Quantification
07:34

Layer Microdissection of Tricuspid Valve Leaflets for Biaxial Mechanical Characterization and Microstructural Quantification

Published on: February 10, 2022

2.4K

Area of Science:

  • Neuroimaging
  • Biophysical Modeling
  • Signal Processing

Background:

  • Brain tissue microstructure is typically studied using diffusion MRI and MR relaxometry.
  • Current methods rely on signal representations, biophysical models, or regularized inverse Laplace transforms (ILTs).
  • These approaches face challenges with ill-posed problems and disentangling complex compartmental information.

Purpose of the Study:

  • To introduce a general framework for characterizing brain tissue microstructure.
  • To replace ill-posed inverse Laplace transforms (ILTs) with blind source separation (BSS).
  • To enable signal disentanglement for separating the free-water component from diffusion and relaxation data.

Main Methods:

  • Formulating diffusion-relaxation dependence as a blind source separation (BSS) problem.
  • Utilizing physically constrained nonnegative matrix factorization for disentangling information.
  • Applying the framework to diffusion MRI experiments with varying echo times.

Main Results:

  • Demonstrated BSS capability in estimating proton density, compartmental volume fractions, and transversal relaxations via simulations and phantom studies.
  • Validated repeatability and reproducibility of the BSS method.
  • Showcased in vivo potential for correcting free-water contamination and estimating tissue parameters.

Conclusions:

  • Blind source separation (BSS) offers a novel framework for studying microstructure compartmentalization.
  • The developed method provides a new tool for effective free-water elimination in diffusion MRI.
  • This approach advances the characterization of brain tissue microstructure without relying on diffusion modeling.