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Related Concept Videos

Imaging Studies for Cardiovascular System I:Echocardiography01:17

Imaging Studies for Cardiovascular System I:Echocardiography

321
Cardiac imaging studies encompass a wide range of noninvasive and minimally invasive techniques designed to visualize the heart's structure and function in detail. One such technique is echocardiography, which uses high-frequency ultrasound waves to produce detailed images of the heart, known as echocardiograms.
Indications: Echocardiography is utilized to diagnose heart failure, valve disorders, and myocardial infarction. It also assesses cardiac structures' size, shape, and motion,...
321
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  3. Research Domains
  5. Engineering
  7. Geomatic Engineering
  9. Surveying (incl. Hydrographic Surveying)
  11. Automatic Multi-view Pose Estimation In Focused Cardiac Ultrasound.
  1. Home
  3. Research Domains
  5. Engineering
  7. Geomatic Engineering
  9. Surveying (incl. Hydrographic Surveying)
  11. Automatic Multi-view Pose Estimation In Focused Cardiac Ultrasound.

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Automatic multi-view pose estimation in focused cardiac ultrasound.

João Freitas1, João Gomes-Fonseca2, Ana Claudia Tonelli3

  • 1Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal; ICVS/3B's - PT Government Associate Laboratory, Braga/Guimarães, Portugal; Algoritmi Center, School of Engineering, University of Minho, Guimarães, Portugal.

Medical Image Analysis
|March 27, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a new framework to automatically determine the 3D spatial relationships of Focused Cardiac Ultrasound (FoCUS) images. This innovation enables advanced 3D quantitative analysis for improved cardiac assessments.

Keywords:
Deep learningFocused cardiac ultrasoundLine detectionThree-dimensional pose estimation

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Area of Science:

  • Medical Imaging
  • Cardiovascular Ultrasound
  • Artificial Intelligence in Medicine

Background:

  • Focused Cardiac Ultrasound (FoCUS) is a crucial point-of-care tool for cardiovascular assessment.
  • Current FoCUS exams are predominantly qualitative 2D due to equipment and operator limitations.
  • There is a need for quantitative 3D assessments in FoCUS.

Purpose of the Study:

  • To develop a novel framework for automatically estimating the 3D spatial relationships between standard FoCUS views.
  • To enable quantitative 3D analysis and improve the diagnostic capabilities of FoCUS.

Main Methods:

  • A multi-view U-Net-like fully convolutional neural network was employed.
  • The network regressed line-based heatmaps to identify image intersections.
  • A system of nonlinear equations was solved to determine the relative 3D pose between FoCUS views.

    • Main Results:

    • The proposed framework successfully estimated the relative 3D poses of FoCUS images.
    • Validation on a realistic in silico FoCUS dataset demonstrated promising accuracy.
    • Preliminary experiments confirmed the feasibility of 3D image analysis methods.

    Conclusions:

    • The developed framework enables automatic 3D spatial relationship estimation for FoCUS.
    • This approach facilitates the transition from qualitative 2D to quantitative 3D FoCUS assessments.
    • The method holds potential for enhancing diagnostic accuracy and clinical utility of FoCUS.