Automated reference-free detection of motion artifacts in magnetic resonance images



Our objectives were to provide an automated method for spatially resolved detection and quantification of motion artifacts in MR images of the head and abdomen as well as a quality control of the trained architecture.

Materials and methods

T1-weighted MR images of the head and the upper abdomen were acquired in 16 healthy volunteers under rest and under motion. Images were divided into overlapping patches of different sizes achieving spatial separation. Using these patches as input data, a convolutional neural network (CNN) was trained to derive probability maps for the presence of motion artifacts. A deep visualization offers a human-interpretable quality control of the trained CNN. Results were visually assessed on probability maps and as classification accuracy on a per-patch, per-slice and per-volunteer basis.


On visual assessment, a clear difference of probability maps was observed between data sets with and without motion. The overall accuracy of motion detection on a per-patch/per-volunteer basis reached 97%/100% in the head and 75%/100% in the abdomen, respectively.


Automated detection of motion artifacts in MRI is feasible with good accuracy in the head and abdomen. The proposed method provides quantification and localization of artifacts as well as a visualization of the learned content. It may be extended to other anatomic areas and used for quality assurance of MR images.

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Author information




Küstner: protocol/project development, data collection or management, data analysis; Liebgott: data analysis; Mauch: data analysis; Martirosian: protocol/project development, data collection or management; Bamberg: data collection or management; Nikolaou: data collection or management; Yang: protocol/project development, data analysis; Schick: protocol/project development; and Gatidis: data collection or management, data analysis

Corresponding author

Correspondence to Thomas Küstner.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

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This article does not contain any studies with animals performed by any of the authors.

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Informed consent was obtained from all individual participants included in the study.

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Küstner, T., Liebgott, A., Mauch, L. et al. Automated reference-free detection of motion artifacts in magnetic resonance images. Magn Reson Mater Phy 31, 243–256 (2018).

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  • Machine learning
  • Neural networks
  • Artifacts
  • Quality assurance