Classification of moving coronary calcified plaques based on motion artifacts using convolutional neural networks: a robotic simulating study on influential factors

Magdalena Dobrolińska, Niels van der Werf, Marcel Greuter, Beibei Jiang, Riemer Slart, Xueqian Xie*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

3 Citations (Scopus)
25 Downloads (Pure)


Background: Motion artifacts afect the images of coronary calcifed plaques. This study utilized convolutional neural
networks (CNNs) to classify the motion-contaminated images of moving coronary calcifed plaques and to determine
the infuential factors for the classifcation performance.
Methods: Two artifcial coronary arteries containing four artifcial plaques of diferent densities were placed on
a robotic arm in an anthropomorphic thorax phantom. Each artery moved linearly at velocities ranging from 0 to
60 mm/s. CT examinations were performed with four state-of-the-art CT systems. All images were reconstructed with
fltered back projection and at least three levels of iterative reconstruction. Each examination was performed at 100%,
80% and 40% radiation dose. Three deep CNN architectures were used for training the classifcation models. A fvefold cross-validation procedure was applied to validate the models.
Results: The accuracy of the CNN classifcation was 90.2±3.1%, 90.6±3.5%, and 90.1±3.2% for the artifcial plaques
using Inception v3, ResNet101 and DenseNet201 CNN architectures, respectively. In the multivariate analysis, higher
density and increasing velocity were signifcantly associated with higher classifcation accuracy (all P<0.001). The classifcation accuracy in all three CNN architectures was not afected by CT system, radiation dose or image reconstruction method (all P>0.05).
Conclusions: The CNN achieved a high accuracy of 90% when classifying the motion-contaminated images into
the actual category, regardless of diferent vendors, velocities, radiation doses, and reconstruction algorithms, which
indicates the potential value of using a CNN to correct calcium scores.
Original languageEnglish
Article number151
Number of pages10
JournalBMC Medical Imaging
Issue number1
Early online date19 Oct 2021
Publication statusPublished - 19 Oct 2021

Bibliographical note

Funding Information:
This study was sponsored by Ministry of Science and Technology of China (project no. 2016YFE0103000), National Natural Science Foundation of China (81971612 and 81471662), Shanghai Jiao Tong University (ZH2018ZDB10). The funders played no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Publisher Copyright:
© 2021, The Author(s).


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