Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Jun 8, 2026

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

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

Published on: November 23, 2019

Registration of longitudinal image sequences with implicit template and spatial-temporal heuristics.

Guorong Wu¹, Qian Wang, Hongjun Jia

¹Department of Radiology and BRIC, University of North Carolina at Chapel Hill, USA. grwu@med.unc.edu

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention

|October 1, 2010

Summary

Related Concept Videos

You might also read

Related Articles

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

Sort by

Same author

Denoising Diffusion-Weighted Images Using Grouped Iterative Hard Thresholding of Multi-Channel Framelets.

Computational diffusion MRI : MICCAI Workshop·2017

Same author

Robust Construction of Diffusion MRI Atlases with Correction for Inter-Subject Fiber Dispersion.

Computational diffusion MRI : MICCAI Workshop·2017

Same author

Robust Fusion of Diffusion MRI Data for Template Construction.

Scientific reports·2017

Same author

Learning-Based Multimodal Image Registration for Prostate Cancer Radiation Therapy.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2017

Same author

Segmenting hippocampal subfields from 3T MRI with multi-modality images.

Medical image analysis·2017

Same author

Joint Discriminative and Representative Feature Selection for Alzheimer's Disease Diagnosis.

Machine learning in medical imaging. MLMI (Workshop)·2017

This study introduces a novel method for simultaneous longitudinal and groupwise image registration. The approach accurately measures anatomical changes over time and aligns multiple subjects

Area of Science:

Medical Imaging
Computational Anatomy
Neuroscience

Background:

Accurate measurement of longitudinal anatomical changes is crucial for clinical studies.
Identifying disease-affected brain regions necessitates population data registration to a common space.

Purpose of the Study:

To develop a novel method for simultaneous longitudinal and groupwise registration of multi-subject longitudinal imaging data.
To consistently measure longitudinal anatomical changes within subjects.
To jointly align all imaging data from all time points and subjects to a hidden common space.

Main Methods:

Introduction of temporal fiber bundles to analyze spatial-temporal anatomical changes within longitudinal datasets.
Development of a probabilistic model incorporating spatial smoothness and temporal continuity on fiber bundles.

More Related Videos

Profiling Maternal Behavior Responses During Whole-Brain Imaging

Profiling Maternal Behavior Responses During Whole-Brain Imaging

Published on: January 24, 2025

Long-Term Imaging of Identified Neural Populations using Microprisms in Freely Moving and Head-Fixed Animals

Long-Term Imaging of Identified Neural Populations using Microprisms in Freely Moving and Head-Fixed Animals

Published on: January 19, 2024

Related Experiment Videos

Last Updated: Jun 8, 2026

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

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

Published on: November 23, 2019

Profiling Maternal Behavior Responses During Whole-Brain Imaging

Profiling Maternal Behavior Responses During Whole-Brain Imaging

Published on: January 24, 2025

Long-Term Imaging of Identified Neural Populations using Microprisms in Freely Moving and Head-Fixed Animals

Long-Term Imaging of Identified Neural Populations using Microprisms in Freely Moving and Head-Fixed Animals

Published on: January 19, 2024

Simultaneous estimation of transformation fields using the expectation-maximization (EM) algorithm and maximum a posteriori (MAP) estimation.

Main Results:

The proposed method successfully measures longitudinal changes in anatomical structures.
Quantitative analysis of hippocampus volume changes demonstrated the method's effectiveness.
The new method outperformed conventional pairwise registration techniques.

Conclusions:

The developed simultaneous longitudinal and groupwise registration method offers improved accuracy in measuring anatomical changes over time.
This approach is valuable for clinical studies, particularly in identifying disease-affected brain regions.
The method shows significant potential for advancing neuroimaging analysis in disease research.