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

4.8K
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...
4.8K
Imaging Studies for Cardiovascular System V: CT01:28

Imaging Studies for Cardiovascular System V: CT

58
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...
58
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

35
DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
35

You might also read

Related Articles

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

Sort by
Same author

[Literature data mining on the current research status of uveitis in China].

[Zhonghua yan ke za zhi] Chinese journal of ophthalmology·2024
Same author

MRgFUS ablation of a recurrent tenosynovial giant cell tumor in the foot using ExAblate 2100 system in combination with patient immobilization device.

Radiography (London, England : 1995)·2024
Same author

Uncertainties and opportunities in delivering environmentally sustainable surgery: the surgeons' view.

Anaesthesia·2024
Same author

Multi-Stage Adaptive Spline Autofocus (MASA) with a Learned Metric for Deformable Motion Compensation in Interventional Cone-Beam CT.

Proceedings of SPIE--the International Society for Optical Engineering·2023
Same author

Surgical navigation for guidewire placement from intraoperative fluoroscopy in orthopaedic surgery.

Physics in medicine and biology·2023
Same author

A pilot study to assess the impact of aboriginal and torres strait islander cultural humility webinars on australian medical school students.

BMC medical education·2023

Related Experiment Video

Updated: Aug 21, 2025

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

Targeted Deformable Motion Compensation for Vascular Interventional Cone-Beam CT Imaging.

A Sisniega1, A Lu1, H Huang1

  • 1Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD USA.

Proceedings of Spie--The International Society for Optical Engineering
|November 16, 2022
PubMed
Summary

This study introduces targeted autofocus for better visualization of vascular structures during interventional radiology procedures. The method improves the identification of small vessels by compensating for motion artifacts in cone-beam CT imaging.

Keywords:
cone-beam CTintraoperative imagingmotion compensationsoft-tissue imaging

More Related Videos

Dual-phase Cone-beam Computed Tomography to See, Reach, and Treat Hepatocellular Carcinoma during Drug-eluting Beads Transarterial Chemo-embolization
09:49

Dual-phase Cone-beam Computed Tomography to See, Reach, and Treat Hepatocellular Carcinoma during Drug-eluting Beads Transarterial Chemo-embolization

Published on: December 2, 2013

10.4K
Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

42.7K

Related Experiment Videos

Last Updated: Aug 21, 2025

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
Dual-phase Cone-beam Computed Tomography to See, Reach, and Treat Hepatocellular Carcinoma during Drug-eluting Beads Transarterial Chemo-embolization
09:49

Dual-phase Cone-beam Computed Tomography to See, Reach, and Treat Hepatocellular Carcinoma during Drug-eluting Beads Transarterial Chemo-embolization

Published on: December 2, 2013

10.4K
Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

42.7K

Area of Science:

  • Medical Imaging
  • Interventional Radiology
  • Image Processing

Background:

  • Cone-beam CT is vital for 3D guidance in interventional radiology, particularly for vascular procedures.
  • Deformable motion during image acquisition degrades image quality and obscures small vessels.
  • Existing autofocus methods lack specificity for vascular imaging tasks.

Purpose of the Study:

  • To develop a deformable motion compensation method specifically targeted for vascular imaging.
  • To enhance the visibility of small vascular structures in cone-beam CT scans.
  • To improve the accuracy of interventional radiology procedures.

Main Methods:

  • A two-stage framework involving 2D projection data analysis and 3D volume space delineation.
  • A novel autofocus approach utilizing a vesselness metric based on 3D image Hessian properties.
  • A cost metric promoting vesselness and spatial sparsity within the region of interest.

Main Results:

  • Effective restoration of vascular shape and contrast in simulated data, reducing artifacts.
  • Significant improvement in segmentation accuracy (up to 42% increase in DICE coefficient).
  • Enhanced visibility of liver vasculature in clinical datasets from transarterial chemoembolization procedures.

Conclusions:

  • Targeted motion compensation shows promise for improved identification of small vascular structures.
  • Autofocus metrics tailored for vascular imaging enable reliable motion compensation.
  • The method preserves anatomical integrity while enhancing visualization of critical vascular anatomy.