Automated detection of motion artefacts in MR imaging using decision forests
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
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 k-space data according to a motion trajectory, using the three common k-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.
Date Issued
2017-06-11
Date Acceptance
2017-05-14
Citation
Journal of Medical Entomology, 2017, 2017
ISSN
0022-2585
Publisher
Oxford University Press (OUP)
Journal / Book Title
Journal of Medical Entomology
Volume
2017
Copyright Statement
© 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.
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/28695126
Publication Status
Published
Coverage Spatial
United States
Article Number
4501647
Date Publish Online
2017-06-11