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Retrospective Cardiac Gating with A Prototype Small-Animal X-ray Computed Tomograph
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An Adaptive Seismocardiography (SCG)-ECG Multimodal Framework for Cardiac Gating Using Artificial Neural Networks.

Jingting Yao1, S Tridandapani2, W F Auffermann3

  • 1School of Electrical and Computer EngineeringGeorgia Institute of TechnologyAtlantaGA30332USA.

IEEE Journal of Translational Engineering in Health and Medicine
|November 9, 2018
PubMed
Summary
This summary is machine-generated.

A new multimodal framework combining electrocardiography (ECG) and seismocardiography (SCG) significantly improves cardiac quiescence prediction for coronary computed tomography angiography (CCTA) gating. This fusion-based approach enhances diagnostic quality and radiation dose reduction compared to traditional ECG-only methods.

Keywords:
Artificial neural networkscardiac gatingcardiac quiescencecomputed tomography angiographycoronary angiographyechocardiographyelectrocardiographymultimodal gatingseismocardiography

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Area of Science:

  • Cardiovascular Imaging
  • Biomedical Engineering
  • Artificial Intelligence in Medicine

Background:

  • Coronary computed tomography angiography (CCTA) prospective gating aims to reduce radiation exposure and improve image quality.
  • Current ECG-only gating methods can be limited by heart rate variability and suboptimal quiescence prediction.
  • Seismocardiography (SCG) offers complementary information to ECG for cardiac cycle analysis.

Purpose of the Study:

  • To develop and evaluate a multimodal framework integrating ECG and SCG for enhanced CCTA prospective gating.
  • To assess the improvement in cardiac quiescence prediction accuracy and diagnostic quality compared to ECG-only methods.
  • To investigate the robustness of the fusion-based approach against heart rate variability.

Main Methods:

  • A three-layer artificial neural network adaptively fuses ECG and SCG signals for beat-by-beat quiescence prediction.
  • The framework was tested on healthy subjects and cardiac patients undergoing CCTA.
  • Diagnostic quality of reconstructed CCTA volumes was assessed by a radiologist using Likert scales.

Main Results:

  • The fusion-based ECG-SCG method improved cardiac quiescence prediction by 47% compared to ECG-only prediction, validated against echocardiography.
  • Seventeen out of 18 participants benefited from the fusion-based prediction.
  • Fusion-based prediction demonstrated greater resistance to heart rate variability and improved diagnostic quality of CCTA.
  • One patient with noisy SCG data showed better results with ECG-only prediction.

Conclusions:

  • The multimodal ECG-SCG framework offers a more personalized and reliable approach to cardiac quiescence prediction for CCTA gating.
  • ECG-only gating may be suboptimal, and its performance can be significantly enhanced by incorporating SCG data.
  • This fusion strategy holds promise for optimizing CCTA acquisition protocols, reducing radiation dose, and improving diagnostic accuracy.