Affiliations: [a] Department of Radiology, University Medical Centre Maribor, Maribor, Slovenia | [b] Center for Clinical Physiology, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia | [c] Faculty of Health Sciences, University of Ljubljana, Ljubljana, Slovenia
Correspondence:
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Corresponding author: Janez Žibert, Faculty of Health Sciences, University of Ljubljana, Ljubljana, Slovenia. E-mail: [email protected].
Abstract: BACKGROUND: Quality control of magnetic resonance imaging includes image validation, which covers also artefact detection. The daily manual review of magnetic resonance images for possible artefacts can be time-consuming, so automated methods for computer-assisted quality assessment of magnetic resonance imaging need to be developed. OBJECTIVE: The aim of this study was to develop automatic detection of Gibbs artefacts in magnetic resonance imaging using a deep learning method called transfer learning, and to demonstrate the potential of this approach for the development of an automatic quality control tool for the detection of such artefacts in magnetic resonance imaging. METHODS: The magnetic resonance image dataset of the scanned phantom for quality assurance was created using a turbo spin-echo pulse sequence in the transverse plane. Images were created to include Gibbs artefacts of varying intensities. The images were annotated by two independent reviewers. The annotated dataset was used to develop a method for Gibbs artefact detection using the transfer learning approach. The VGG-16, VGG-19, and ResNet-152 convolutional neural networks were used as pre-trained networks for transfer learning and compared using 5-fold cross-validation. RESULTS: All accuracies of the classification models were above 97%, while the AUC values were all above 0.99, confirming the high quality of the constructed models. CONCLUSION: We show that transfer learning can be successfully used to detect Gibbs artefacts on magnetic resonance images. The main advantages of transfer learning are that it can be applied on small training datasets, the procedures to build the models are not so complicated, and they do not require much computational power. This shows the potential of transfer learning for the more general task of detecting artefacts in magnetic resonance images of patients, which consequently can improve and speed up the process of quality assessment in medical imaging practice.
Keywords: Gibbs artefact, transfer learning, automatic detection, image quality control
