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

2.1K
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...
2.1K
Curvilinear Motion: Rectangular Components01:23

Curvilinear Motion: Rectangular Components

837
Curvilinear motion characterizes the movement of a particle or object along a curved path, notably evident when envisioning a car navigating a winding road. If the car starts at point A, its position vector is established within a fixed frame of reference, where the ratio of the position vector to its magnitude signifies the unit vector pointing in the position vector's direction.
As the car advances, its position evolves over time. Quantifying the car's velocity involves computing the...
837
Relation between Poisson's ratio, Modulus of Elasticity and Modulus of Rigidity01:15

Relation between Poisson's ratio, Modulus of Elasticity and Modulus of Rigidity

390
Deformation occurs in axial and transverse directions when an axial load is applied to a slender bar. This deformation impacts the cubic element within the bar, transforming it into either a rectangular parallelepiped or a rhombus, contingent on its orientation. This transformation process induces shearing strain. Axial loading elicits both shearing and normal strains. Applying an axial load instigates equal normal and shearing stresses on elements oriented at a 45° angle to the load axis.
390
Bending of Curved Members - Strain Analysis01:14

Bending of Curved Members - Strain Analysis

323
The mechanics of deformation in curved members, such as beams or arches, under bending moments, involve complex responses. When such a member, symmetric about the y-axis and shaped like a segment of a circle centered at point C, is subjected to equal and opposite forces, its curvature and surface lengths change significantly. This alteration results in the shift of the curvature's center from C to C', indicating a tighter curve.
The important part of bending analysis for such a member...
323
Three-Dimensional Analysis of Strain01:29

Three-Dimensional Analysis of Strain

410
Three-dimensional strain analysis is crucial for understanding how materials deform under stress, particularly in elastic, homogeneous materials. This method employs principal stress axes to simplify complex stress states into more understandable forms. Subjected to stress, a small cubic element within a material either expands or contracts along these axes, transforming into a rectangular parallelepiped. This transformation effectively illustrates the material's deformation. The principal...
410
Castigliano's Theorem01:18

Castigliano's Theorem

675
Castigliano's theorem analyzes displacements and rotations in elastic structures. It relates the derivative of elastic strain energy to the applied forces or moments, allowing for the calculation of deformations. The theorem states that the partial derivative of the total strain energy of a system with respect to a specific load results in the displacement at the point where the load is applied. This principle applies to both forces and moments.
675

You might also read

Related Articles

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

Sort by
Same author

A digital twin framework for adaptive treatment planning in radiotherapy.

Physics in medicine and biology·2026
Same author

Stereotactic arrhythmia radioablation for refractory ventricular tachycardia: A narrative review and pooled analysis of clinical outcomes and treatment delivery approaches.

Journal of applied clinical medical physics·2026
Same author

The effect of interfractional variation on delivered dose with ultrahypofractionated pencil beam scanning proton therapy for localized prostate cancer.

Journal of applied clinical medical physics·2025
Same author

Linear Federated Learning for Outcome Prediction With Application to Hepatocellular Carcinoma Radiotherapy.

JCO clinical cancer informatics·2025
Same author

Photon-counting CT in cancer radiotherapy: technological advances and clinical benefits.

Physics in medicine and biology·2025
Same author

Predictive Model of Acute Rectal Toxicity in Prostate Cancer Treated With Radiotherapy.

JCO clinical cancer informatics·2025

Related Experiment Video

Updated: Nov 8, 2025

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

43.2K

A generalized framework for analytic regularization of uniform cubic B-spline displacement fields.

Keyur D Shah1, James A Shackleford1, Nagarajan Kandasamy1

  • 1Electrical and Computer Engineering Department, Drexel University, Philadelphia, PA 19104, United States of America.

Biomedical Physics & Engineering Express
|April 20, 2021
PubMed
Summary

This study introduces an efficient analytical framework for image registration regularization, significantly outperforming numerical methods. The new approach ensures physically meaningful transformations with substantial speed improvements.

Keywords:
B-spline registrationanalytic regularizationdeformable registrationregularization

More Related Videos

Using Digital Image Correlation to Characterize Local Strains on Vascular Tissue Specimens
09:29

Using Digital Image Correlation to Characterize Local Strains on Vascular Tissue Specimens

Published on: January 24, 2016

9.6K
Quantification of Strain in a Porcine Model of Skin Expansion Using Multi-View Stereo and Isogeometric Kinematics
14:14

Quantification of Strain in a Porcine Model of Skin Expansion Using Multi-View Stereo and Isogeometric Kinematics

Published on: April 16, 2017

11.8K

Related Experiment Videos

Last Updated: Nov 8, 2025

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

43.2K
Using Digital Image Correlation to Characterize Local Strains on Vascular Tissue Specimens
09:29

Using Digital Image Correlation to Characterize Local Strains on Vascular Tissue Specimens

Published on: January 24, 2016

9.6K
Quantification of Strain in a Porcine Model of Skin Expansion Using Multi-View Stereo and Isogeometric Kinematics
14:14

Quantification of Strain in a Porcine Model of Skin Expansion Using Multi-View Stereo and Isogeometric Kinematics

Published on: April 16, 2017

11.8K

Area of Science:

  • Medical imaging
  • Computational anatomy
  • Image processing

Background:

  • Image registration is ill-posed, requiring regularization for meaningful results.
  • Numerical regularization is computationally expensive, limiting its application.
  • Existing methods struggle with computational efficiency for large image datasets.

Purpose of the Study:

  • To develop a computationally efficient analytical framework for image registration regularization.
  • To support multiple regularization methods within a unified mathematical framework.
  • To validate the accuracy and speed of the proposed analytical approach.

Main Methods:

  • Utilized cubic B-splines for the image registration transform.
  • Developed a generalized mathematical framework for five distinct regularizers: diffusion, curvature, linear elastic, third-order, and total displacement.
  • Compared analytical solutions against their numerical counterparts for accuracy and performance.

Main Results:

  • The analytical framework accurately reproduces results from numerical methods.
  • Analytic solutions demonstrated significant speed improvements, up to two orders of magnitude faster than numerical implementations.
  • The generalized framework efficiently handles various regularization techniques.

Conclusions:

  • The proposed analytical framework offers a computationally efficient and accurate solution for image registration regularization.
  • This method accelerates the process of achieving physically meaningful transformations in medical imaging.
  • The findings pave the way for faster and more robust image registration in various applications.