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Related Concept Videos

Imaging Studies for Cardiovascular System V: CT01:28

Imaging Studies for Cardiovascular System V: CT

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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...
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Electrocardiogram01:29

Electrocardiogram

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An electrocardiogram (ECG or EKG) is a critical diagnostic tool that records the electrical signals produced by the heart during each heartbeat. This recording is achieved through electrodes placed strategically on the arms, legs, and chest. The electrocardiograph amplifies these signals and produces 12 distinct tracings, offering a comprehensive understanding of the heart's electrical activity.
Three major waveforms are present in a typical ECG recording: the P wave, the QRS complex, and...
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Related Experiment Video

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Retrospective Cardiac Gating with A Prototype Small-Animal X-ray Computed Tomograph
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An Adaptive SCG-ECG Multimodal Gating Framework for Cardiac CTA.

Shambavi Ganesh1, Mostafa Abozeed2, Usman Aziz2

  • 1Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA. shambavi.ganesh@gatech.edu.

Journal of Imaging Informatics in Medicine
|October 15, 2024
PubMed
Summary
This summary is machine-generated.

A new method uses seismocardiograms and ECGs to predict heart quiet times for better cardiac CT angiography (CTA) imaging. This approach significantly improves diagnostic accuracy for cardiovascular disease (CVD) detection.

Keywords:
Artificial neural network (ANN)Cardiac computed tomography angiography (CTA)Cardiovascular diseaseElectrocardiography (ECG)Seismocardiography (SCG)Ultrasound (US)

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Area of Science:

  • Biomedical Engineering
  • Cardiovascular Imaging
  • Artificial Intelligence in Medicine

Background:

  • Cardiovascular disease (CVD) is a leading global cause of mortality.
  • Current diagnostic methods like catheter coronary angiography (CCA) are invasive and costly.
  • Cardiac computed tomography angiography (CTA) offers a less invasive alternative but is limited by motion artifacts due to poor cardiac motion capture.

Purpose of the Study:

  • To develop and validate a novel multimodal approach for enhancing CTA image quality.
  • To improve the prediction of cardiac quiescent periods for more accurate CTA acquisition.
  • To reduce motion artifacts and enhance diagnostic capabilities for coronary artery disease (CAD).

Main Methods:

  • A weighted fusion (WF) approach integrating seismocardiogram (SCG) and electrocardiogram (ECG) data was used.
  • Artificial neural networks (ANNs), including a regression-based framework (r-ANN WF) and a classification-based framework (c-ANN WF), were developed.
  • The proposed methods were compared against traditional ECG gating and ultrasound (US) data for accuracy and computational efficiency.

Main Results:

  • The r-ANN WF approach demonstrated a 52.6% improvement in cardiac quiescence prediction accuracy compared to ECG-based methods, validated against US data.
  • The average prediction time for the r-ANN WF method was 4.83 ms, indicating suitability for real-time applications.
  • Reconstructed CTA images using both r-ANN WF and c-ANN WF achieved diagnostic quality comparable to US-based gating.

Conclusions:

  • The proposed multimodal approach using SCG and ECG significantly enhances the prediction of cardiac quiescent periods for CTA.
  • The r-ANN WF framework offers a computationally efficient and accurate method for improving CTA diagnostic quality.
  • This innovative technique holds significant clinical potential for more accurate and efficient diagnosis and management of cardiovascular diseases.