Motion Artifact Detection for T1-Weighted Brain MR Images Using Convolutional Neural Networks
International Journal of Neural Systems, vol. 34, pp. 2450052
Abstract
Quality assessment (QA) of magnetic resonance imaging (MRI) encompasses several factors such as noise, contrast, homogeneity, and imaging artifacts. Quality evaluation is often not standardized and relies on the expertise, and vigilance of the personnel, posing limitations especially with large datasets. Machine learning based on convolutional neural networks (CNNs) is a promising approach to address these challenges by performing automated inspection of MR images. In this study, a CNN for the detection of random head motion artifacts (RHM) in T1-weighted MRI as one aspect of image quality is proposed. A two-step approach aimed to first identify images exhibiting pronounced motion artifacts, and second to evaluate the feasibility of a more detailed three-class classification. The utilized dataset consisted of 420 T1-weighted whole-brain image volumes with isotropic resolution. Human experts assigned each volume to one of three classes of artifact prominence. Results demonstrate an accuracy of 95% for the identification of images with pronounced artifact load. The addition of an intermediate class retained an accuracy of 76%. The findings highlight the potential of CNN-based approaches to increase the efficiency of post-hoc QAs in large datasets by flagging images with potentially relevant artifact loads for closer inspection.
Authors 9
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Affiliation as printed
Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen, Germany
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Affiliation as printed
Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen, Germany
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Jana Zweerings Aachen Department of Psychiatry, Psychotherapy and Psychosomatics Faculty of Medicine
Affiliation as printed
Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen, Germany
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Affiliation as printed
Philips Healthcare, Aachen, Germany
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Forschungszentrum Jülich · Düsseldorf University Hospital · Heinrich Heine University Düsseldorf
Affiliation as printed
Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
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Arnim Johannes Gaebler Aachen Department of Psychiatry, Psychotherapy and Psychosomatics Faculty of Medicine Institute of Neurophysiology
Jülich Aachen Research Alliance · RWTH Aachen University
Affiliation as printed
Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen, Germany
Institute of Neurophysiology, Faculty of Medicine, RWTH Aachen, Germany
JARA-BRAIN, Jülich Aachen Research Alliance (JARA), Translational Brain Medicine, Germany
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Patrick D. Eisner Aachen Department of Psychiatry, Psychotherapy and Psychosomatics Faculty of Medicine
Affiliation as printed
Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen, Germany
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Pegah Sarkheil Aachen Department of Psychiatry, Psychotherapy and Psychosomatics Faculty of Medicine
Affiliation as printed
Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen, Germany
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Jülich Aachen Research Alliance · RWTH Aachen University
Affiliation as printed
Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen, Germany
JARA-BRAIN, Jülich Aachen Research Alliance (JARA), Translational Brain Medicine, Germany
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