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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

374
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
374
Curvilinear Motion: Rectangular Components01:23

Curvilinear Motion: Rectangular Components

1.4K
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...
1.4K

You might also read

Related Articles

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

Sort by
Same author

The genetic architecture of multimodal human brain age.

Nature communications·2024
Same author

Distance-weighted Sinkhorn loss for Alzheimer's disease classification.

iScience·2024
Same author

Integrating imaging and genomic data for the discovery of distinct glioblastoma subtypes: a joint learning approach.

Scientific reports·2024
Same author

Plasma Biomarkers as Predictors of Progression to Dementia in Individuals with Mild Cognitive Impairment.

Journal of Alzheimer's disease : JAD·2024
Same author

Genetic and Clinical Correlates of AI-Based Brain Aging Patterns in Cognitively Unimpaired Individuals.

JAMA psychiatry·2024
Same author

Dimensional Neuroimaging Endophenotypes: Neurobiological Representations of Disease Heterogeneity Through Machine Learning.

ArXiv·2024
Same journal

LEARNABLE HIERARCHICAL VISUAL CONTEXTS FOR TUMOR SEGMENTATION IN COMPUTED TOMOGRAPHY IMAGES.

Proceedings. IEEE International Symposium on Biomedical Imaging·2026
Same journal

DUAL CROSS-ATTENTION SIAMESE TRANSFORMER FOR RECTAL TUMOR REGROWTH ASSESSMENT IN WATCH-AND-WAIT ENDOSCOPY.

Proceedings. IEEE International Symposium on Biomedical Imaging·2026
Same journal

LUMEN: LONGITUDINAL MULTI-MODAL RADIOLOGY MODEL FOR PROGNOSIS AND DIAGNOSIS.

Proceedings. IEEE International Symposium on Biomedical Imaging·2026
Same journal

OVERVIEW OF THE CXR-LT 2026 CHALLENGE: MULTI-CENTER LONG-TAILED AND ZERO SHOT CHEST X-RAY CLASSIFICATION.

Proceedings. IEEE International Symposium on Biomedical Imaging·2026
Same journal

CROSS-MODAL FINE-TUNING OF 3D CONVOLUTIONAL FOUNDATION MODELS FOR ADHD CLASSIFICATION WITH LOW-RANK ADAPTATION.

Proceedings. IEEE International Symposium on Biomedical Imaging·2026
Same journal

AN IN SILICO STUDY OF LOW-INTENSITY FOCUSED ULTRASOUND DISPLACEMENT MAPPING WITH A 220 KHZ CLINICAL PHASED-ARRAY TRANSDUCER.

Proceedings. IEEE International Symposium on Biomedical Imaging·2026
See all related articles

Related Experiment Video

Updated: Feb 28, 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

NONRIGID VOLUME REGISTRATION USING SECOND-ORDER MRF MODEL.

Dongjin Kwon1,2, Il Dong Yun3, Kilian M Pohl2

  • 1School of EECS, ASRI, Seoul Nat'l Univ., Seoul, Korea.

Proceedings. IEEE International Symposium on Biomedical Imaging
|June 20, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a novel nonrigid registration method using second-order smoothness priors in Markov Random Field models. This approach enhances accuracy and smoothness in 3D displacement vector fields for medical imaging.

Keywords:
MRF Energy ModelNonrigid Volume RegistrationSecond-Order Smoothness Prior

More Related Videos

Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease
09:30

Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease

Published on: December 18, 2016

20.2K
Topographical Estimation of Visual Population Receptive Fields by fMRI
06:02

Topographical Estimation of Visual Population Receptive Fields by fMRI

Published on: February 3, 2015

9.7K

Related Experiment Videos

Last Updated: Feb 28, 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
Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease
09:30

Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease

Published on: December 18, 2016

20.2K
Topographical Estimation of Visual Population Receptive Fields by fMRI
06:02

Topographical Estimation of Visual Population Receptive Fields by fMRI

Published on: February 3, 2015

9.7K

Area of Science:

  • Medical image analysis
  • Computational anatomy
  • Computer vision

Background:

  • Nonrigid registration is crucial for comparing medical images.
  • First-order smoothness priors in registration models have limitations, such as fronto-parallel effects.
  • Accurate deformation field estimation is essential for quantitative analysis.

Purpose of the Study:

  • To introduce a novel nonrigid registration method utilizing second-order smoothness priors within a Markov Random Field (MRF) energy model.
  • To address the limitations of first-order priors in capturing natural deformation smoothness.
  • To improve the accuracy and quality of estimated displacement vector fields.

Main Methods:

  • Developed a nonrigid registration method based on an MRF energy model.
  • Incorporated second-order smoothness priors to model spatial relationships between nodes.
  • Defined labels in the MRF as 3D displacement vectors.
  • Evaluated the method on uni- and multi-modal Brain MRI volumes.

Main Results:

  • Second-order smoothness priors generate smoother displacement vector fields compared to first-order priors.
  • The proposed method mitigates fronto-parallel effects often seen with first-order priors.
  • Experimental results demonstrate more accurate registration outcomes.

Conclusions:

  • The MRF energy model with second-order smoothness priors offers superior performance for nonrigid registration.
  • This method provides more accurate and smoother deformation field estimation.
  • The approach is effective for both uni- and multi-modal Brain MRI registration.