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

Computed Tomography01:10

Computed Tomography

7.6K
Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
7.6K
Imaging Studies for Cardiovascular System V: CT01:28

Imaging Studies for Cardiovascular System V: CT

671
Cardiac computed tomography (CT) scanning is an advanced cardiac imaging technique that utilizes CT technology, with or without intravenous (IV) contrast, to produce accurate cross-sectional virtual slices of specific areas of the heart, coronary circulation, and major blood vessels such as the aorta, pulmonary veins, and arteries. The computer processes these slices to generate three-dimensional images. Multidetector CT (MDCT) is a rapid form of CT scanning that captures multiple slices...
671

You might also read

Related Articles

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

Sort by
Same author

Transcriptional Heterogeneity of Oligodendrocytes: Molecular Basis of Diversity Across Development, Brain Regions, and Neurological Diseases.

Neurology international·2026
Same author

Nivolumab plus Ipilimumab versus Lenvatinib or Sorafenib as First-Line Treatment for Unresectable Hepatocellular Carcinoma: CheckMate 9DW Japanese Subgroup Analysis.

Liver cancer·2026
Same author

Neuroinflammation and treatment resistance in major depressive disorder.

Frontiers in pharmacology·2026
Same author

Feasibility of Peroral Pancreatoscopy Using the 9-Fr eyeMAX for Surgical Planning in Main-Duct and Mixed-Type Intraductal Papillary Mucinous Neoplasms.

Diagnostics (Basel, Switzerland)·2026
Same author

Evaluation of the Efficacy of Combined Treatment of Liver Cancer With Losartan and Ultrasound‑Stimulated Microbubble Cavitation.

Ultrasound in medicine & biology·2026
Same author

Overall Survival Among Patients With Hepatocellular Carcinoma Treated With External Beam Radiation Therapy: Individual Patient Data Outcomes From a Multinational Cohort.

Journal of clinical oncology : official journal of the American Society of Clinical Oncology·2026

Related Experiment Video

Updated: May 4, 2026

Improved Registration of 3D CT Angiography with X-ray Fluoroscopy for Image Fusion During Transcatheter Aortic Valve Implantation
06:59

Improved Registration of 3D CT Angiography with X-ray Fluoroscopy for Image Fusion During Transcatheter Aortic Valve Implantation

Published on: June 3, 2018

10.7K

RANSAC-based global 3DUS to CT/MR rigid registration using liver surface and vessels.

Tsubasa Goto1, Riki Igarashi2, Iku Cho2

  • 1Medical Systems Research and Development Center, Fujifilm Corporation, 6-15-6 Minami-aoyama, Minato-ku, Tokyo, 107-0062, Japan. tsubasa.goto@fujifilm.com.

International Journal of Computer Assisted Radiology and Surgery
|August 21, 2025
PubMed
Summary

This study introduces an automated 3D-3D registration method for liver fusion imaging, independent of ultrasound sweep position. The technique enhances workflow efficiency for physicians and sonographers.

Keywords:
Fusion imagingLiverPoint cloudsRigid registrationUltrasound

More Related Videos

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

8.1K
Author Spotlight: Advancing Hepatic Fibrosis Diagnosis Using Magnetic Resonance Elastography and AI
06:09

Author Spotlight: Advancing Hepatic Fibrosis Diagnosis Using Magnetic Resonance Elastography and AI

Published on: July 21, 2023

1.4K

Related Experiment Videos

Last Updated: May 4, 2026

Improved Registration of 3D CT Angiography with X-ray Fluoroscopy for Image Fusion During Transcatheter Aortic Valve Implantation
06:59

Improved Registration of 3D CT Angiography with X-ray Fluoroscopy for Image Fusion During Transcatheter Aortic Valve Implantation

Published on: June 3, 2018

10.7K
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

8.1K
Author Spotlight: Advancing Hepatic Fibrosis Diagnosis Using Magnetic Resonance Elastography and AI
06:09

Author Spotlight: Advancing Hepatic Fibrosis Diagnosis Using Magnetic Resonance Elastography and AI

Published on: July 21, 2023

1.4K

Area of Science:

  • Medical Imaging
  • Image Registration
  • Computational Anatomy

Background:

  • Fusion imaging combines ultrasound (US) with CT/MR for enhanced visualization.
  • Accurate initial registration is crucial but challenging due to variable US probe positions.
  • Current methods lack a standardized approach for automatic registration across all US sweep angles.

Purpose of the Study:

  • To develop an automatic, position-independent global rigid 3D-3D registration technique for liver fusion imaging.
  • To overcome limitations of manual registration and improve workflow efficiency.
  • To create a robust method adaptable to various ultrasound acquisition geometries.

Main Methods:

  • Utilized deep learning for segmenting liver and vessels (portal, hepatic veins) in US and CT/MR images.
  • Extracted point clouds of liver surface and vessel centerlines for landmark identification.
  • Employed RANSAC-based point cloud registration with incorporated constraints for speed and robustness.

Main Results:

  • Quantitative evaluation on 80 patients demonstrated registration accuracy across intercostal, subcostal, and epigastric US views.
  • Achieved mean registration errors of 7.3±3.2 mm (intercostal), 9.3±3.7 mm (subcostal), and 8.4±3.9 mm (epigastric).
  • The method proved effective regardless of the ultrasound sweep position.

Conclusions:

  • The proposed global rigid registration technique automates complex manual registration for liver fusion imaging.
  • Significantly enhanced workflow efficiency for physicians and sonographers.
  • Provides a reliable and automated solution for pre-procedural planning and intra-procedural guidance.