Motion-corrected coronary calcium scores by a CNN: a robotic simulating study

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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European Radiology