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Motion artifact recognition and quantification in coronary CT angiography using convolutional neural networks.

T Lossau1, H Nickisch2, T Wissel2

  • 1Philips Research, Hamburg, Germany; Hamburg University of Technology, Germany.

Medical Image Analysis
|November 25, 2018
PubMed
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Deep learning models can now accurately detect and quantify cardiac motion artifacts in coronary CT angiography (CCTA) images. This technology enhances diagnostic reliability and image quality assessment for better patient care.

Keywords:
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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Cardiovascular Imaging

Background:

  • Diagnostic non-invasive coronary CT angiography (CCTA) requires excellent image quality.
  • Cardiac motion artifacts can compromise CCTA diagnostic accuracy and treatment planning.

Purpose of the Study:

  • To develop deep learning-based methods for recognizing and quantifying coronary motion artifacts in CCTA.
  • To assess the diagnostic reliability and image quality of CCTA using these novel measures.
  • To enable the application, steering, and evaluation of motion compensation algorithms.

Main Methods:

  • A Coronary Motion Forward Artifact model for CT data (CoMoFACT) was developed to simulate motion artifacts.
  • CoMoFACT generated training data from 17 prospectively ECG-triggered clinical cases with controlled motion levels.
  • Convolutional neural networks (CNNs) were trained for artifact classification and motion level regression.

Main Results:

  • CNNs achieved 93.3% ± 1.8% accuracy in classifying motion-free versus motion-perturbed coronary image patches.
  • A regression network predicted the target motion level with a mean absolute error of 1.12 ± 0.07.
  • The methods demonstrated transferability and generalization on CCTA cases with real motion artifacts.

Conclusions:

  • Deep learning effectively recognizes and quantifies coronary motion artifacts in CCTA.
  • These quantitative measures improve diagnostic reliability and image quality assessment.
  • The developed approach supports the optimization of motion compensation strategies in CCTA.