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

Long-Term Storage of Formalin-Fixed Paraffin-Embedded Tissues Negatively Impacts Next-Generation Sequencing: Successful Restoration with a DNA Repair Enzyme.

Clinical chemistry·2026
Same author

Feasibility Study of Balloon Angioplasty for the Treatment of Ophthalmic Artery Stenoses in Patients With Geographic Atrophy.

Ophthalmic surgery, lasers & imaging retina·2026
Same author

Flow Diversion for Middle Cerebral Artery Aneurysms: Clinical and Angiographic Results from a Single-Center Experience.

AJNR. American journal of neuroradiology·2026
Same author

Usefulness of Non-Contrast Electron Density Imaging for the Identification of Brain Tissue Changes in Large Vessel Occlusion Acute Ischemic Stroke.

Journal of integrative neuroscience·2026
Same author

Woven Endobridge device for ruptured vs. unruptured aneurysms: insights from the WorldWideWEB study.

Neuroradiology·2026
Same author

Continuous Patient Monitoring in the Catheterization Laboratory: Usability and Operational Impact Assessment.

Cureus·2026
Same journal

Multi-view constrained semi-supervised vertebra detection for 3D ultrasound spine volume.

Medical physics·2026
Same journal

Accuracy of quantitative <sup>177</sup>Lu SPECT/CT imaging: A systematic review.

Medical physics·2026
Same journal

Physics-constrained dual-domain network for CBCT reconstruction from orthogonal X-rays in gynecologic radiotherapy.

Medical physics·2026
Same journal

Decomposition-based harmonization for quantitative PET imaging across scanners and radiotracers.

Medical physics·2026
Same journal

Development and evaluation of an in vivo dose-based monitoring system for electron FLASH radiation therapy.

Medical physics·2026
Same journal

A novel optical respiratory gating system with a hybrid phase-amplitude algorithm for spot-scanning proton therapy.

Medical physics·2026
See all related articles

Related Experiment Video

Updated: Jun 5, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.7K

Tunable and real-time automatic interventional x-ray collimation from semi-supervised deep feature extraction.

Brian C Lee1, Damini Rijhwani1, Sydney Lang1

  • 1Philips Research, Cambridge, Massachusetts, USA.

Medical Physics
|December 6, 2024
PubMed
Summary
This summary is machine-generated.

An automated collimation algorithm for interventional X-ray procedures significantly reduces radiation exposure for patients and staff. This tunable system integrates seamlessly into workflows, maintaining procedure quality and time while enhancing safety.

Keywords:
collimationendovascular surgeryinterventional radiologyx‐ray

More Related Videos

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

42.5K
X-ray Dose Reduction through Adaptive Exposure in Fluoroscopic Imaging
08:30

X-ray Dose Reduction through Adaptive Exposure in Fluoroscopic Imaging

Published on: September 11, 2011

14.4K

Related Experiment Videos

Last Updated: Jun 5, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.7K
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

42.5K
X-ray Dose Reduction through Adaptive Exposure in Fluoroscopic Imaging
08:30

X-ray Dose Reduction through Adaptive Exposure in Fluoroscopic Imaging

Published on: September 11, 2011

14.4K

Area of Science:

  • Medical Imaging
  • Interventional Radiology
  • Health Physics

Background:

  • Endovascular procedures are increasingly common, relying on c-arm X-ray systems for image guidance.
  • Fluoroscopy during these procedures poses radiation risks to patients and staff, with workflow modifications potentially impacting procedure time or quality.
  • Current collimation practices are underutilized due to manual complexity and varying interventionalist preferences.

Purpose of the Study:

  • To develop and evaluate an automated collimation system to improve image quality and reduce radiation exposure in interventional X-ray procedures.
  • To address the challenges of manual collimation, including its cumbersome nature and the difficulty of parameter manipulation during procedures.
  • To create a system that accommodates diverse collimation preferences across different procedures and anatomies.

Main Methods:

  • Proposed a tunable algorithm for automatic collimation using a region-of-interest optimizer.
  • Integrated image, system, device, and radiation features into the algorithm.
  • Developed a method with a clear mapping between algorithm parameters and practical outcomes, implemented with deep feature extraction via a convolutional neural network.

Main Results:

  • Demonstrated real-time implementation and evaluated performance on simulated and clinical radial access procedure datasets.
  • Showcased potential for radiation reduction and assessed the impact of a mixed supervision training strategy.
  • Reader studies with expert radiologists confirmed 100% clinical acceptability, high quality ratings, and improved radiation protection.

Conclusions:

  • The modular design of the automated collimation algorithm met user requirements without disrupting workflow or procedure time.
  • The system shows strong potential for reducing radiation risks to patients and operators.
  • Further evaluation in diverse clinical settings is recommended to support clinical translation.