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The authors of this study aimed to classify motion-induced blurred images of calcified coronary plaques, in order to correct coronary calcium scored on non-triggered chest computed tomography (CT). They did so by using a deep convolutional neural network (CNN) which was trained using a selection of images of motion artifacts.

Key points

  • A deep CNN architecture trained by CT images of motion artifacts showed the ability to correct coronary calcium scores from blurred images.
  • A correction algorithm based on deep CNN can be used for a tenfold reduction in Agatston score variations from 38 to 3.7% of moving coronary calcified plaques and to improve the sensitivity from 65 to 85% for the detection of calcifications.
  • This experimental study provides a method to improve its accuracy for coronary calcium scores that is a fundamental step towards a real clinical scenario.

Article: Motion-corrected coronary calcium scores by a convolutional neural network: a robotic simulating study

Authors: Yaping Zhang, Niels R. van der Werf, Beibei Jiang, Robbert van Hamersvelt, Marcel J.W. Greuter, Xueqian Xie

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Welcome to the blog on Artificial Intelligence of the European Society of Radiology

This blog aims at bringing educational and critical perspectives on AI to readers. It should help imaging professionals to learn and keep up to date with the technologies being developed in this rapidly evolving field.