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Evaluation of Left Ventricular Structure and Function using 3D Echocardiography
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Automatic 3D left atrial strain extraction framework on cardiac computed tomography.

Ling Chen1, Sung-Hao Huang2, Tzu-Hsiang Wang1

  • 1Institute of Hospital and Health Care Administration, National Yang Ming Chiao Tung University, Taipei, Taiwan.

Computer Methods and Programs in Biomedicine
|May 22, 2024
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Summary

This study introduces an automated deep learning framework for 3D left atrial strain analysis using cardiac CT, offering a novel alternative to echocardiography for evaluating cardiac function.

Keywords:
Cardiac computed tomographyLA segmentationLA strain extractionSubclinical atrial fibrillation

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Area of Science:

  • Cardiology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Left atrial (LA) strain analysis is crucial for myocardial function assessment, but echocardiography has limitations due to anatomical constraints.
  • Cardiac computed tomography (CT) offers superior spatial resolution for in-depth LA function evaluation.
  • Previous CT-based strain studies relied on manual methods and focused primarily on the left ventricle (LV).

Purpose of the Study:

  • To develop a fully automatic deep learning (DL) framework for 3D LA strain extraction from cardiac CT.
  • To overcome the limitations of manual ROI selection and echocardiography in LA strain analysis.
  • To enable comprehensive 3D assessment of LA function using cardiac CT.

Main Methods:

  • Developed a 3D strain extraction framework using a 2.5D GN-U-Net for LA segmentation on cardiac CT.
  • Incorporated axis-oriented 3D view extraction and LA strain measurement.
  • Validated segmentation accuracy (DSC) and compared LA volumes/EF with ground truth (ICC, r, B-A plots).
  • Evaluated automatically extracted LA strains against 2D echocardiography measurements.
  • Assessed the impact of atrial fibrillation (AF) burden on LA strain using atrial high rate episode (AHRE) burden.

Main Results:

  • The GN-U-Net achieved high LA segmentation accuracy (DSC=0.9603).
  • Framework-extracted LA volumes and EF showed excellent agreement with expert measurements (ICCs >0.902).
  • LA strains demonstrated moderate agreement with 2D echocardiography (ICCs >0.703).
  • Significantly lower global strain and LAEF were observed in patients with higher AHRE burden (> 6 min).

Conclusions:

  • The DL-based framework enables automatic and feasible 3D LA strain extraction from cardiac CT.
  • This automated tool offers a promising 3D alternative to echocardiography for assessing LA function.
  • The framework's ability to correlate strain with AF burden highlights its clinical utility.