Table of Contents
Journal of Medical Engineering
Volume 2017, Article ID 4501647, 9 pages
Research Article

Automated Detection of Motion Artefacts in MR Imaging Using Decision Forests

1Pattern Recognition Lab, Friedrich-Alexander University Erlangen-Nürnberg, 91058 Erlangen, Germany
2Biomedical Image Analysis Group, Imperial College London, London SW7 2AZ, UK

Correspondence should be addressed to Benedikt Lorch; ed.uaf@hcrol.tkideneb

Received 4 March 2017; Revised 7 May 2017; Accepted 14 May 2017; Published 11 June 2017

Academic Editor: Norio Iriguchi

Copyright © 2017 Benedikt Lorch et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The acquisition of a Magnetic Resonance (MR) scan usually takes longer than subjects can remain still. Movement of the subject such as bulk patient motion or respiratory motion degrades the image quality and its diagnostic value by producing image artefacts like ghosting, blurring, and smearing. This work focuses on the effect of motion on the reconstructed slices and the detection of motion artefacts in the reconstruction by using a supervised learning approach based on random decision forests. Both the effects of bulk patient motion occurring at various time points in the acquisition on head scans and the effects of respiratory motion on cardiac scans are studied. Evaluation is performed on synthetic images where motion artefacts have been introduced by altering the -space data according to a motion trajectory, using the three common -space sampling patterns: Cartesian, radial, and spiral. The results suggest that a machine learning approach is well capable of learning the characteristics of motion artefacts and subsequently detecting motion artefacts with a confidence that depends on the sampling pattern.