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

Generalized Hooke's Law01:22

Generalized Hooke's Law

2.8K
The generalized Hooke's Law is a broadened version of Hooke's Law, which extends to all types of stress and in every direction. Consider an isotropic material shaped into a cube subjected to multiaxial loading. In this scenario, normal stresses are exerted along the three coordinate axes. As a result of these stresses, the cubic shape deforms into a rectangular parallelepiped. Despite this deformation, the new shape maintains equal sides, and there is a normal strain in the direction of the...
2.8K
Routh-Hurwitz Criterion I01:15

Routh-Hurwitz Criterion I

617
Consider an electrical power grid, where stability is essential to prevent blackouts. The Routh-Hurwitz criterion is a valuable tool for assessing system stability under varying load conditions or faults. By analyzing the closed-loop transfer function, the Routh-Hurwitz criterion helps determine whether the system remains stable.
To apply the Routh-Hurwitz criterion, a Routh table is constructed. The table's rows are labeled with powers of the complex frequency variable s, starting from the...
617
Routh-Hurwitz Criterion II01:19

Routh-Hurwitz Criterion II

1.1K
In the application of the Routh-Hurwitz criterion, two specific scenarios can arise that complicate stability analysis.
The first scenario occurs when a singular zero appears in the first column of the Routh table. This situation creates a division by zero issues. To resolve this, a small positive or negative number, denoted as epsilon (∈), is substituted for the zero. The stability analysis proceeds by assuming a sign for ∈. If ∈ is positive, any sign change in the first...
1.1K
Constraints and Statical Determinacy01:26

Constraints and Statical Determinacy

1.0K
In structural engineering, the equilibrium of a system is not only determined by its equations of equilibrium but also with the help of constraints. Constraints refer to restrictions on the motion of a system. The proper combinations of constraints can minimize the total number of constraints needed to maintain a system in mechanical equilibrium. When this happens, the system is said to be statically determinate. For such systems, the unknown reaction supports can be estimated using equilibrium...
1.0K
¹H NMR of Conformationally Flexible Molecules: Variable-Temperature NMR01:15

¹H NMR of Conformationally Flexible Molecules: Variable-Temperature NMR

1.7K
The axial and equatorial protons in cyclohexane can be distinguished by performing a variable-temperature NMR experiment. In this process, except for one proton, the remaining eleven protons are replaced by deuterium. The deuterium substitution avoids the possible peak splitting caused by the spin-spin coupling between the adjacent protons. The remaining proton flips between the axial and equatorial positions.
1.7K
Application of Nonlinear Inequalities01:29

Application of Nonlinear Inequalities

268
A nonlinear inequality describes a comparison involving an expression that curves or behaves more complexly than a straight line. These inequalities often appear in forms that include squares, products, or variables in the denominator.To solve such an inequality, one starts by rewriting it so that zero appears on one side. For example, the inequality:  can be factored as: This form makes it easier to identify the values that cause the expression to equal zero. In this case, the...
268

You might also read

Related Articles

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

Sort by
Same author

LNODE: latent dynamics reveal the shared spatiotemporal structure of amyloid-$β$ progression.

ArXiv·2026
Same author

LNODE: Uncovering the Latent Dynamics of <math><mi>A</mi> <mi>β</mi></math> in Alzheimer's Disease.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2026
Same author

A boundary integral method for simulating the dynamics of inextensible vesicles suspended in a viscous fluid in 2D.

Journal of computational physics·2025
Same author

Aligning personalized biomarker trajectories onto a common time axis: a connectome-based ODE model for Tau-Amyloid beta dynamics.

Medical image analysis·2025
Same author

Inverse Problem Regularization for 3D Multi-Species Tumor Growth Models.

International journal for numerical methods in biomedical engineering·2025
Same author

A single-snapshot inverse solver for two-species graph model of tau pathology spreading in human Alzheimer's disease.

Brain informatics·2025

Related Experiment Video

Updated: Feb 20, 2026

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function
02:09

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Published on: April 12, 2024

1.1K

Constrained H1-regularization schemes for diffeomorphic image registration.

Andreas Mang1, George Biros1

  • 1The Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas, 78712-0027, US.

SIAM Journal on Imaging Sciences
|October 28, 2017
PubMed
Summary
This summary is machine-generated.

We developed new regularization methods for deformable image registration, allowing precise control over deformation compressibility and shear. This enhances registration accuracy by avoiding oversmoothing and enabling tailored deformation characteristics.

Keywords:
35Q9349J2065K1068U1076D55Krylov methodconstrained regularization schemesinexact Newtonoptimal controlshear controlstationary velocity field diffeomorphic registrationvariable eliminationvolume conservation

More Related Videos

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
07:13

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Published on: October 27, 2023

1.7K
Four-Dimensional CT Analysis Using Sequential 3D-3D Registration
05:05

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

Published on: November 23, 2019

8.5K

Related Experiment Videos

Last Updated: Feb 20, 2026

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function
02:09

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Published on: April 12, 2024

1.1K
Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
07:13

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Published on: October 27, 2023

1.7K
Four-Dimensional CT Analysis Using Sequential 3D-3D Registration
05:05

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

Published on: November 23, 2019

8.5K

Area of Science:

  • Medical image analysis
  • Computational mathematics
  • Scientific computing

Background:

  • Deformable image registration is crucial for medical image analysis.
  • Standard regularization methods can lead to oversmoothing and limit control over deformation properties.
  • Controlling deformation characteristics like compressibility and shear is essential for accurate registration.

Purpose of the Study:

  • To propose novel regularization schemes for deformable image registration.
  • To develop efficient algorithms for numerical approximation of these schemes.
  • To enable explicit control over the compressibility and shear of deformation maps.

Main Methods:

  • Image registration treated as a variational optimal control problem.
  • Parametrization of deformation map by its velocity.
  • Tikhonov regularization augmented with divergence constraint on velocity field.
  • Inversion for stationary velocity field and mass source map.
  • Globalized, preconditioned, matrix-free, reduced space (Gauss-)Newton-Krylov optimization scheme.
  • Variable elimination techniques for reduced system unknowns.

Main Results:

  • Demonstrated explicit control over the determinant of the deformation gradient (compressibility).
  • Introduced a new scheme to control shear deformation.
  • Numerical experiments showed no compromise in registration quality with added control.
  • Avoided oversmoothing of the deformation map.
  • Successfully promoted or penalized shear while controlling deformation gradient determinant.

Conclusions:

  • The proposed regularization schemes offer enhanced control over deformable registration.
  • The efficient numerical methods allow for practical implementation.
  • These methods provide a powerful tool for precise medical image analysis and manipulation.