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Related Concept Videos

Imaging Studies for Cardiovascular System III: X-Ray01:20

Imaging Studies for Cardiovascular System III: X-Ray

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The most common cardiovascular diagnostic test is an X-ray. It produces images of the heart, blood vessels, and adjacent structures.
Definition and Purpose
An X-ray, or radiograph, is a non-invasive method that uses ionizing radiation to take images of internal structures. It is mainly used in cardiac imaging to examine the heart, lungs, and major blood vessels, aiming to identify abnormalities in the heart's size, shape, and position, such as heart failure, congenital defects, and vascular...
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Non-fluoroscopic Catheter Tracking for Fluoroscopy Reduction in Interventional Electrophysiology
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Using a convolutional neural network for human recognition in a staff dose management software for fluoroscopic

J Troville1, R S Dhonde1, S Rudin1

  • 1The State University of New York at Buffalo, Jacobs School of Medicine and Biomedical Sciences, Canon Stroke and Vascular Research Center, 875 Ellicott St., Buffalo, NY 14203.

Proceedings of Spie--The International Society for Optical Engineering
|March 18, 2021
PubMed
Summary

This study introduces a novel system for tracking radiation exposure in medical staff during procedures. It uses a convolutional neural network (CNN) to accurately identify individuals and record their cumulative radiation dose for improved safety.

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Area of Science:

  • Medical Physics
  • Radiological Sciences
  • Biomedical Engineering

Background:

  • Staff dose management is critical in fluoroscopically-guided interventional (FGI) procedures.
  • Lack of awareness of radiation scatter levels increases stochastic and deterministic risks for healthcare professionals.
  • Existing systems may not provide individualized or real-time radiation dose monitoring for staff.

Purpose of the Study:

  • To develop and validate a system for individualized radiation dose recording for staff in FGI procedures.
  • To utilize human recognition via a convolutional neural network (CNN) for accurate dose tracking.
  • To enhance radiation safety protocols through real-time feedback and cumulative dose reporting.

Main Methods:

  • A scattered-radiation display system (SDS) with a controller-area network (CAN) bus interface was employed.
  • Time-of-flight depth sensing camera (Microsoft Kinect V2) captured depth maps for body tracking.
  • A CNN was trained on binary body masks for human recognition, achieving 97.3% testing accuracy, even with protective attire.

Main Results:

  • The CNN accurately identified unique body shape features for individualized dose recording.
  • The system demonstrated high prediction accuracy (97.3%) irrespective of obstructing objects like face masks and lead aprons.
  • Individualized cumulative dose reports were generated for eye lens, waist, and collar levels, with slight positional error increase when protective attire was worn.

Conclusions:

  • Individualized cumulative dose reporting using CNN-based human recognition enhances radiation dose management in clinical settings.
  • Real-time feedback and accurate tracking of staff radiation exposure are crucial for mitigating risks.
  • The developed system offers a promising solution for improving safety in FGI procedures.