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

Chromatographic Resolution01:15

Chromatographic Resolution

In chromatography, a solute moves through a chromatographic column and tends to spread, forming a Gaussian-shaped band. The longer the solute spends in the column, the broader the band becomes. The broadening can lead to overlaps within the column, affecting separation effectiveness.
The effectiveness of separation can be evaluated by determining the level of separation between two neighboring peaks in a chromatogram, which represents the individual components of a sample.
In chromatography,...
Distance Problem01:29

Distance Problem

When an object's velocity changes over time, the total distance traveled can be determined by summing small displacement intervals over short increments. This approach approximates the true distance through numerical summation and the use of integral calculus. An estimate of the total displacement can be obtained by measuring velocity at regular intervals and multiplying each value by the corresponding time step.If a runner accelerates over the first three seconds of a race, speed measurements...
Orthogonal Trajectories01:26

Orthogonal Trajectories

Orthogonal trajectories describe the geometric relationship between two families of curves that intersect each other at right angles. One illustrative case involves a family of parabolas that open sideways along the x-axis. These curves share a common shape but differ by a scaling parameter, resulting in a set of curves that all pass through the origin and widen at different rates.Determining Orthogonal TrajectoriesTo identify the orthogonal trajectories for these parabolas, the first step...
Quadric Surfaces01:28

Quadric Surfaces

Quadric surfaces are three-dimensional surfaces characterized by second-degree equations in the variables x, y, and z. These surfaces are smooth and continuous, and specific combinations of squared and linear terms define their shapes. The main types of quadric surfaces include ellipsoids, cones, paraboloids, and hyperboloids. Each type exhibits distinct geometric features depending on how the variables are arranged and related within the equation.Ellipsoids are closed surfaces formed when all...
Space Curves01:25

Space Curves

A space curve describes the path followed by a particle moving through three-dimensional space. Unlike plane curves, which are confined to two coordinates, space curves require three coordinate functions. If t is a parameter, the position of the particle is represented by the vector function\begin{equation*}\mathbf{r}(t)=\langle x(t),y(t),z(t)\rangle,\end{equation*}where x(t), y(t), and z(t) are differentiable functions of t. As t varies over an interval, the endpoints of the position vectors...
Divergence Theorem in 3D Space01:20

Divergence Theorem in 3D Space

In vector calculus, flux measures the total flow of a vector field through a surface. For a closed surface in three-dimensional space, this means measuring how much of the field passes outward through every point on the boundary. Directly calculating this flux can be difficult when the surface has a complicated or irregular shape. The Divergence Theorem provides a powerful alternative by relating surface flux to behavior inside the enclosed region.The Divergence Theorem states that the outward...

You might also read

Related Articles

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

Sort by
Same author

[Choosing wisely in Swedish neuroradiology].

Lakartidningen·2026
Same author

[Brain imaging in cognitive disorders - from diagnosis to treatment and monitoring].

Lakartidningen·2026
Same author

Intraoperative 3D quantitative magnetic resonance imaging in paediatric brain tumour surgery.

PloS one·2026
Same author

Mapping brain tumor microstructure: A multimodal study of diffusion MRI, intraoperative fluorescence, and neuropathology in navigated biopsies.

NeuroImage. Clinical·2025
Same author

On the sampling strategies and models for measuring diffusion exchange with a double diffusion encoding sequence.

Magnetic resonance letters·2025
Same author

Quantitative MRI relaxometry in brain tumor needle biopsies: Multimodal comparison with tissue fluorescence, radiology, and neuropathology.

PloS one·2025
Same journal

Dimerization-induced conformational transitions of yeast iso-1 cytochrome <i>c</i>.

Magnetic resonance letters·2026
Same journal

NMR insights into the role of urea in cellulose dissolution: A stoichiometric study of cellobiose in NaOH/urea aqueous solution.

Magnetic resonance letters·2026
Same journal

Lighting up β-cell function: Noninvasive MRI of glucose-stimulated Zn<sup>2+</sup> secretion.

Magnetic resonance letters·2026
Same journal

Production of isotope-labeled nanobody KN035 in <i>Pichia pastoris</i> yields native-like conformation and PD-L1 binding activity comparable to KN035 expressed in mammalian cells.

Magnetic resonance letters·2026
Same journal

FlexCENT: A frequency-flexible CEST imaging network combining frequency offset encoding and three-dimensional U-Net.

Magnetic resonance letters·2026
Same journal

Single-scan ultra-selective probing on targeted components from overlapped NMR spectra.

Magnetic resonance letters·2026
See all related articles

Related Experiment Video

Updated: Jul 2, 2026

Mapping Molecular Diffusion in the Plasma Membrane by Multiple-Target Tracing MTT
12:19

Mapping Molecular Diffusion in the Plasma Membrane by Multiple-Target Tracing MTT

Published on: May 27, 2012

17.7K

Diffusivity-limited q-space trajectory imaging.

Deneb Boito1,2, Magnus Herberthson3, Tom Dela Haije4

  • 1Department of Biomedical Engineering, Campus US, Linköping University, Linköping, SE-581 83, Sweden.

Magnetic Resonance Letters
|September 8, 2025
PubMed
Summary
This summary is machine-generated.

Q-space trajectory imaging (QTI) now offers more reliable microstructural analysis. New upper bound constraints enhance parameter accuracy in diffusion MRI, improving brain imaging results.

Keywords:
ConstrainedDiffusionDiffusion MRIMicroscopic anisotropyMicrostructureQTIQTI+q-space trajectory imaging

More Related Videos

The Diffusion of Passive Tracers in Laminar Shear Flow
08:01

The Diffusion of Passive Tracers in Laminar Shear Flow

Published on: May 1, 2018

9.0K
Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules
10:20

Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules

Published on: September 5, 2019

8.8K

Related Experiment Videos

Last Updated: Jul 2, 2026

Mapping Molecular Diffusion in the Plasma Membrane by Multiple-Target Tracing MTT
12:19

Mapping Molecular Diffusion in the Plasma Membrane by Multiple-Target Tracing MTT

Published on: May 27, 2012

17.7K
The Diffusion of Passive Tracers in Laminar Shear Flow
08:01

The Diffusion of Passive Tracers in Laminar Shear Flow

Published on: May 1, 2018

9.0K
Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules
10:20

Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules

Published on: September 5, 2019

8.8K

Area of Science:

  • Biomedical Engineering
  • Neuroimaging
  • Materials Science

Background:

  • Diffusion magnetic resonance imaging (dMRI) with generalized gradient waveforms enables Q-space trajectory imaging (QTI) for non-invasive microstructural estimation.
  • The QTI+ framework enhanced QTI reliability by incorporating positivity constraints, improving resilience to noise and data sparsity.

Purpose of the Study:

  • To expand the constraints used in the QTI model fitting process.
  • To investigate the impact of additional upper bound diffusivity constraints on parameter estimation accuracy.

Main Methods:

  • Applied a constrained estimation framework to QTI data.
  • Introduced new constraints, including an upper bound on diffusivity values, into the QTI fitting model.
  • Validated the enhanced method on a public human brain dataset and data from healthy volunteers.

Main Results:

  • The expanded set of constraints, particularly the upper bound on diffusivity, further improved the accuracy of retrieved microstructural parameters.
  • The enhanced QTI method demonstrated improved performance on both public and volunteer datasets.

Conclusions:

  • Incorporating upper bound diffusivity constraints into the QTI+ framework significantly enhances the reliability and accuracy of microstructural parameter estimation.
  • This refined QTI approach offers improved non-invasive characterization of porous media, with direct applications in neuroimaging.