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

State Space Representation01:27

State Space Representation

The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models00:57

Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models

Physiological pharmacokinetic models, often called flow-limited or perfusion models, typically assume a swift drug distribution between tissue and venous blood, creating a rapid drug equilibrium. This premise is based on the idea that drug diffusion is extremely fast, and the cell membrane presents no barrier to drug permeation. In this scenario, where no drug binding occurs, the drug concentration in the tissue equals that of the venous blood leaving the tissue. This greatly simplifies the...
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
Pharmacodynamic Models: Overview01:27

Pharmacodynamic Models: Overview

Pharmacodynamic (PD) responses describe the interaction between a drug and its biological target, culminating in a physiological effect. These responses can be classified into different types: continuous variables, such as blood glucose levels; categorical outcomes, like survival rates; and time-to-event metrics, such as disease progression. Understanding and modeling PD responses are critical for optimizing drug efficacy and safety.PD models describe the relationship between drug concentration...

You might also read

Related Articles

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

Sort by
Same author

Inter-Shot Motion Correction of Segmented 3D-GRASE ASL Perfusion Imaging With Self-Navigation and CAIPI.

Magnetic resonance in medicine·2026
Same author

MR Spectroscopy Without Water Suppression Using the Gradient Impulse Response Function.

Magnetic resonance in medicine·2026
Same author

Modelling Motion-Induced Signal Corruption in Steady-State Diffusion MRI.

Magnetic resonance in medicine·2026
Same author

Combined caLculation of Ultra-high field Biases (CLUB) With Sandwich: Fast, Simultaneous Estimation of 3D B<sub>0</sub> and Multi-Channel B<sub>1</sub> <sup>+</sup> Maps at 7 T.

Magnetic resonance in medicine·2026
Same author

Mesoscale imaging of the human cerebellum reveals converging regional specialization of its morphology, vasculature, and cytoarchitecture.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Comparison of cardiac diffusion MRI using multiple prospective respiratory motion correction techniques.

Magnetic resonance in medicine·2025
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 10, 2026

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients
09:32

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

Published on: December 18, 2016

12.4K

An extended phase graph-based framework for DANTE-SPACE simulations including physiological, temporal, and spatial

Matthijs H S de Buck1,2, Peter Jezzard1, Aaron T Hess1

  • 1Wellcome Centre for Integrative Neuroimaging, FMRIB Division, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.

Magnetic Resonance in Medicine
|March 12, 2024
PubMed
Summary
This summary is machine-generated.

A new simulation framework for Delay Alternating with Nutation for Tailored Excitation-Sampling Perfection with Application-Optimized Contrasts (DANTE-SPACE) MRI improves understanding of intracranial vessel wall imaging. This tool aids in optimizing parameters for better contrast and explains signal variations observed in vivo.

Keywords:
DANTE‐SPACEMRIextended phase graphvessel wall imaging

More Related Videos

Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0
07:41

Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0

Published on: June 5, 2017

9.9K
Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
10:44

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

490

Related Experiment Videos

Last Updated: May 10, 2026

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients
09:32

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

Published on: December 18, 2016

12.4K
Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0
07:41

Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0

Published on: June 5, 2017

9.9K
Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
10:44

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

490

Area of Science:

  • Magnetic Resonance Imaging (MRI)
  • Medical Physics
  • Biomedical Engineering

Background:

  • Delay Alternating with Nutation for Tailored Excitation-Sampling Perfection with Application-Optimized Contrasts (DANTE-SPACE) is used for 3D intracranial vessel wall imaging.
  • Clinical application is limited by signal variations in vessel wall, cerebrospinal fluid (CSF), and blood.
  • Optimizing sequence parameters is crucial for achieving desired image contrast.

Purpose of the Study:

  • To introduce a comprehensive DANTE-SPACE simulation framework.
  • To understand the underlying contrast mechanisms in DANTE-SPACE imaging.
  • To facilitate improved parameter selection and contrast optimization for intracranial vessel wall imaging.

Main Methods:

  • Developed an extended phase graph formalism for efficient spin ensemble simulation of the DANTE-SPACE sequence.
  • Incorporated physiological processes: pulsatile flow velocity, varying flow directions, intravoxel velocity variation, diffusion, and B1 effects.
  • Modeled mechanisms responsible for observed signal levels in DANTE-SPACE.

Main Results:

  • Intravoxel velocity variation enhanced temporal stability and robustness.
  • Pulsatile velocity variation and diffusion significantly affected CSF signal.
  • Low-velocity pulsatility in CSF and vessel wall explained observed in vivo signal heterogeneity.

Conclusions:

  • The developed simulation framework enables comprehensive optimization of DANTE-SPACE parameters.
  • The framework aids in explaining observed contrasts in acquired DANTE-SPACE data.
  • Facilitates improved intracranial vessel wall imaging through better parameter selection and understanding of contrast mechanisms.