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

You might also read

Related Articles

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

Sort by
Same author

Multimodal Cross-Attention Fusion of B-Mode Ultrasound and Strain Elastography for Tumor Segmentation in Robotics-Assisted Surgery.

IEEE transactions on medical robotics and bionics·2026
Same author

Real-Time SLAM-Based Correction and 3D Visualization for Fluorescence Lifetime Imaging.

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

Real-time margin assessment for video-assisted thoracic surgery: A pilot clinical trial.

JTCVS techniques·2025
Same author

Integration of Trackerless Surface Reconstruction-Based Surgical Navigation With Exoscopic Trans-Mastoid Surgery.

Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery·2025
Same author

Seeking Common Ground While Reserving Differences: Multiple Anatomy Collaborative Framework for Undersampled MRI Reconstruction.

IEEE journal of biomedical and health informatics·2025
Same author

SLAM-based Breast Reconstruction System for Surgical Guidance Using a Low-Cost Camera.

IEEE transactions on medical robotics and bionics·2025
Same journal

MAP Image Recovery with Guarantees using Locally Convex Multi-Scale Energy (LC-MUSE) Model.

Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference)·2026
Same journal

EARLY DETECTION OF COGNITIVE DECLINE USING VOICE ASSISTANT COMMANDS.

Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference)·2025
Same journal

CROSS-DOMAIN DIFFUSION BASED SPEECH ENHANCEMENT FOR VERY NOISY SPEECH.

Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference)·2025
Same journal

CROSS-DOMAIN SPEECH ENHANCEMENT WITH A NEURAL CASCADE ARCHITECTURE.

Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference)·2025
Same journal

ESTIMATING DIRECTED SPECTRAL INFORMATION FLOW BETWEEN MULTI-RESOLUTION TIME SERIES.

Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference)·2025
Same journal

NEURAL CASCADE ARCHITECTURE FOR JOINT ACOUSTIC ECHO AND NOISE SUPPRESSION.

Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference)·2025
See all related articles

Related Experiment Video

Updated: Oct 25, 2025

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

1.4K

UNSUPERVISED MULTIMODAL IMAGE REGISTRATION WITH ADAPTATIVE GRADIENT GUIDANCE.

Zhe Xu1,2, Jiangpeng Yan1, Jie Luo2

  • 1Shenzhen International Graduate School, Tsinghua University, Shenzhen, China.

Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference)
|August 9, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a new multimodal image registration framework that improves organ boundary alignment by using gradient intensity maps alongside original image data. This approach enhances accuracy in image-guided therapies.

Keywords:
Multimodal image registrationgradient guidanceunsupervised registration

More Related Videos

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.3K
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

757

Related Experiment Videos

Last Updated: Oct 25, 2025

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

1.4K
Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.3K
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

757

Area of Science:

  • Medical imaging
  • Computer vision
  • Machine learning

Background:

  • Multimodal image registration (MIR) is crucial for image-guided therapies.
  • Unsupervised learning methods show promise for deformable image registration.
  • Current methods struggle with accurate organ boundary alignment due to reliance on single image pairs.

Purpose of the Study:

  • To develop a novel multimodal registration framework to improve organ boundary alignment.
  • To address limitations of existing unsupervised learning methods in MIR.
  • To enhance the accuracy and reliability of deformation field estimation.

Main Methods:

  • Proposed a novel multimodal registration framework leveraging deformation fields from original image pairs and gradient intensity maps.
  • Introduced a gated fusion module for adaptive fusion of deformation fields.
  • Utilized auxiliary gradient-space guidance to focus on spatial relationships of organ boundaries.

Main Results:

  • The proposed framework demonstrated improved organ boundary alignment compared to existing methods.
  • Experimental results on CT-MRI datasets confirmed the effectiveness of the approach.
  • Gradient-space guidance enhanced the network's focus on critical spatial relationships.

Conclusions:

  • The novel multimodal registration framework effectively improves organ boundary alignment.
  • The proposed gated fusion and gradient-space guidance are key to enhanced performance.
  • This method offers a more accurate and reliable solution for MIR in clinical applications.