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

Volume of Distribution01:20

Volume of Distribution

The apparent volume of distribution (Vd) is a crucial pharmacokinetic parameter representing the hypothetical body fluid volume into which a drug disperses. It is calculated based on the total amount of drug in the body (estimated from the administered dose and bioavailability) divided by the plasma drug concentration. The total amount of drug in the body does not directly refer to the dose given but is derived by accounting for absorption, distribution, metabolism, and excretion processes.
Properties of DTFT I01:24

Properties of DTFT I

In signal processing, Discrete-Time Fourier Transforms (DTFTs) play a critical role in analyzing discrete-time signals in the frequency domain. Various properties of the DTFTs such as linearity, time-shifting, frequency-shifting, time reversal, conjugation, and time scaling help understand and manipulate these signals for different applications.
The linearity property of DTFTs is fundamental. If two discrete-time signals are multiplied by constants a and b respectively, and then combined to...
Clearance Models: Noncompartmental Models01:17

Clearance Models: Noncompartmental Models

Clearance is a pharmacokinetic parameter traditionally defined by compartment models, signifying the rate at which a drug is expelled from the body. However, a noncompartmental model offers an alternative method for assessing clearance, primarily employing empirical data obtained after administering a single drug dose.
The noncompartmental approach capitalizes on extensive sampling data, correlating the volume of distribution to systemic exposure and the administered dosage. This method enables...
Properties of DTFT II01:24

Properties of DTFT II

In the study of discrete-time signal processing, understanding the properties of the Discrete-Time Fourier Transform (DTFT) is crucial for analyzing and manipulating signals in the frequency domain. Several properties, including frequency differentiation, convolution, accumulation, and Parseval's relation, offer powerful tools for signal analysis.
The frequency differentiation property is illustrated by considering a DTFT pair and differentiating both sides with respect to ω. Multiplying by j...
Discrete-time Fourier transform01:26

Discrete-time Fourier transform

The Discrete-Time Fourier Transform (DTFT) is an essential mathematical tool for analyzing discrete-time signals, converting them from the time domain to the frequency domain. This transformation allows for examining the frequency components of discrete signals, providing insights into their spectral characteristics. In the DTFT, the continuous integral used in the continuous-time Fourier transform is replaced by a summation to accommodate the discrete nature of the signal.
One of the notable...
Compartment Models: Single-Compartment Model01:14

Compartment Models: Single-Compartment Model

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...

You might also read

Related Articles

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

Sort by
Same author

Harmonization of slice thickness through resampling improves comparability of MRI-derived neonatal brain volumes.

Frontiers in radiology·2026
Same author

Youth Soccer Participation and Brain Health Outcomes in Adolescent Athletes.

JAMA network open·2026
Same author

Microbiome-behavior coupling shapes infant adaptation to early maternal unpredictability.

Frontiers in microbiology·2026
Same author

Intraoperative Evaluation of Semiautomatic Localization of the Facial Nerve Using Diffusion Tensor Imaging in Patients with Large Vestibular Schwannomas: A Pilot Study.

Journal of neurological surgery reports·2026
Same author

Application of artificial intelligence in paediatric oncology imaging.

Pediatric radiology·2026
Same author

The potential of low-field MRI for global dementia care.

Nature reviews. Neurology·2026

Related Experiment Video

Updated: Jun 5, 2026

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
09:33

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

Partial volume effect as a hidden covariate in DTI analyses.

Sjoerd B Vos1, Derek K Jones, Max A Viergever

  • 1Image Sciences Institute, Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands. sjoerd@isi.uu.nl

Neuroimage
|January 26, 2011
PubMed
Summary

Diffusion tensor imaging (DTI) studies using tractography can be misled by partial volume effects (PVEs). Fiber bundle shape, including thickness and curvature, influences DTI metrics, necessitating their inclusion in analyses to reveal true microstructural changes.

More Related Videos

Diffusion Imaging in the Rat Cervical Spinal Cord
10:46

Diffusion Imaging in the Rat Cervical Spinal Cord

Published on: April 7, 2015

Related Experiment Videos

Last Updated: Jun 5, 2026

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
09:33

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

Diffusion Imaging in the Rat Cervical Spinal Cord
10:46

Diffusion Imaging in the Rat Cervical Spinal Cord

Published on: April 7, 2015

Area of Science:

  • Neuroimaging
  • Diffusion Tensor Imaging (DTI)
  • White Matter Tractography

Background:

  • Diffusion tensor imaging (DTI) and fiber tractography are widely used to study white matter microstructure.
  • Partial volume effects (PVEs) can adversely affect tractography and DTI metrics.
  • The impact of PVEs varies with fiber bundle characteristics like thickness and shape.

Purpose of the Study:

  • To investigate how PVE-related covariates (thickness, orientation, curvature, shape) modulate DTI metrics.
  • To determine if these covariates confound DTI-based findings, particularly in comparisons between populations or correlations with other measures.
  • To emphasize the importance of accounting for these factors in DTI analysis.

Main Methods:

  • Simulations using synthetic diffusion phantoms.
  • Analysis of DTI metrics for the cingulum and corpus callosum in 55 healthy subjects.
  • Correlation analyses examining the influence of covariates like bundle thickness on DTI measures and group comparisons.

Main Results:

  • DTI metrics (fractional anisotropy, mean diffusivity) were significantly modulated by fiber bundle thickness, orientation, and curvature.
  • Inclusion of volume as a covariate altered correlation results between gender and diffusion measures.
  • Fiber bundle shape and size are critical confounding factors in DTI analyses.

Conclusions:

  • PVE-related covariates, such as bundle thickness and curvature, significantly impact DTI metrics.
  • Failure to account for these factors can lead to misinterpretation of microstructural changes.
  • Incorporating PVE-related covariates is essential for accurate DTI analysis and understanding true tissue properties.