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

Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...

You might also read

Related Articles

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

Sort by
Same author

Effects of maternal edible THC consumption on offspring lung growth and function in a rhesus macaque model.

American journal of physiology. Lung cellular and molecular physiology·2025
Same author

MRI assessed placental volume and adverse pregnancy outcomes: Secondary analysis of prospective cohort study.

Placenta·2024
Same author

Magnetic resonance imaging of placental intralobule structure and function in a preclinical nonhuman primate model†.

Biology of reproduction·2024
Same author

Impaired placental hemodynamics and function in a non-human primate model of gestational protein restriction.

Scientific reports·2023
Same author

Chronic prenatal delta-9-tetrahydrocannabinol exposure adversely impacts placental function and development in a rhesus macaque model.

Scientific reports·2022
Same author

Quantitative longitudinal T2* mapping for assessing placental function and association with adverse pregnancy outcomes across gestation.

PloS one·2022
Same journal

Multi-Contrast Human Brain CEST MRI at 11.7 T: First In Vivo Demonstration.

Magnetic resonance in medicine·2026
Same journal

Suppression of Oscillation and Ghosting in RF-Spoiled Gradient-Echo-Based Dynamic Imaging.

Magnetic resonance in medicine·2026
Same journal

A Simple, Dynamic Geometric Phantom for MRI and CT Reconstruction Pipelines: Beyond Shepp-Logan.

Magnetic resonance in medicine·2026
Same journal

7T 3D-EPI PCASL With High SNR Efficiency and Robustness to Through-Plane B<sub>0</sub> Field Gradients.

Magnetic resonance in medicine·2026
Same journal

A Comparison of Tissue Property Values Estimated Using Conventional Cardiac MRF and MT-Cardiac MRF.

Magnetic resonance in medicine·2026
Same journal

Dependence of the Extra-Cellular Diffusion Coefficient on the Fractions of Neurites and Cell Bodies in Gray Matter.

Magnetic resonance in medicine·2026
See all related articles

Related Experiment Video

Updated: May 25, 2026

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

A unified impulse response model for DCE-MRI.

Matthias C Schabel1

  • 1Advanced Imaging Research Center, Oregon Health & Science University, Portland, OR 97239, USA. schabelm@ohsu.edu

Magnetic Resonance in Medicine
|February 2, 2012
PubMed
Summary
This summary is machine-generated.

The gamma capillary transit time (GCTT) model unifies DCE-MRI models, offering insights into tissue heterogeneity. This new model accurately describes brain tumor physiology, enhancing diagnostic capabilities.

More Related Videos

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging
11:28

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Published on: June 30, 2018

Related Experiment Videos

Last Updated: May 25, 2026

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging
11:28

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Published on: June 30, 2018

Area of Science:

  • Biophysics
  • Medical Imaging
  • Pharmacokinetics

Background:

  • Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is crucial for assessing tissue perfusion and vascularity.
  • Existing DCE-MRI models, such as Tofts-Kety and two-compartment exchange, offer different perspectives on contrast agent kinetics.
  • A unified model could provide a more comprehensive understanding of tissue microvasculature.

Purpose of the Study:

  • To introduce and validate the gamma capillary transit time (GCTT) model, a generalized impulse response model for DCE-MRI.
  • To demonstrate the mathematical unification of several existing DCE-MRI models within the GCTT framework.
  • To assess the GCTT model's performance in characterizing tissue heterogeneity and its applicability to in vivo data.

Main Methods:

  • Developed the gamma capillary transit time (GCTT) model, incorporating a parameter (α⁻¹) for capillary transit time distribution width.
  • Compared the GCTT model against Tofts-Kety, extended Tofts-Kety, adiabatic tissue homogeneity, and two-compartment exchange models.
  • Validated the models using Monte Carlo simulations and in vivo DCE-MRI data from human brain tumors (glioblastoma multiforme, pleomorphic xanthoastrocytoma, anaplastic meningioma).

Main Results:

  • The GCTT model successfully unifies four commonly used DCE-MRI models into a single mathematical formalism.
  • Applying the GCTT model to in vivo data resulted in only minor increases in parameter uncertainty and computational cost.
  • Nonparametric impulse response functions measured in human brain tumors were accurately described by the GCTT model.
  • Estimation of the heterogeneity parameter (α⁻¹) was feasible, though higher signal-to-noise ratios (SNR) are needed for statistical significance.

Conclusions:

  • The GCTT model provides a unified framework for DCE-MRI analysis, encompassing existing methodologies.
  • The model accurately characterizes physiological parameters in human brain tumors, offering potential for improved diagnostic information.
  • Further validation with higher SNR data is recommended for robust estimation of tissue heterogeneity using the GCTT model.