Evaluation of motion and its effect on brain magnetic resonance image quality in children
Motion artifacts pose significant problems for the acquisition of MR images in pediatric populations.
To evaluate temporal motion metrics in MRI scanners and their effect on image quality in pediatric populations in neuroimaging studies.
Materials and methods
We report results from a large pediatric brain imaging study that shows the effect of motion on MRI quality. We measured motion metrics in 82 pediatric patients, mean age 13.4 years, in a T1-weighted brain MRI scan. As a result of technical difficulties, 5 scans were not included in the subsequent analyses. A radiologist graded the images using a 4-point scale ranging from clinically non-diagnostic because of motion artifacts to no motion artifacts. We used these grades to correlate motion parameters such as maximum motion, mean displacement from a reference point, and motion-free time with image quality.
Our results show that both motion-free time (as a ratio of total scan time) and average displacement from a position at a fixed time (when the center of k-space was acquired) were highly correlated with image quality, whereas maximum displacement was not as good a predictor. Among the 77 patients whose motion was measured successfully, 17 had average displacements of greater than 0.5 mm, and 11 of those (14.3%) resulted in non-diagnostic images. Similarly, 14 patients (18.2%) had less than 90% motion-free time, which also resulted in non-diagnostic images.
We report results from a large pediatric study to show how children and young adults move in the MRI scanner and the effect that this motion has on image quality. The results will help the motion-correction community in better understanding motion patterns in pediatric populations and how these patterns affect MR image quality.
KeywordsArtifacts Brain Children Motion measurements Motion Magnetic resonance imaging
This work was funded in part by National Institutes of Health grants 5R42MH086984, 1R01EB019483, 5R01EB018988 and 5R01EB013248.
Compliance with ethical standards
Conflicts of interest
- 19.Likert R (1932) A technique for the measurement of attitudes. Arch Psych 22:1–55Google Scholar
- 23.Erem B, Afacan O, Gholipour A et al. (2014) A system identification approach to estimating a dynamic model of head motion for MRI motion correction. Intelligent MRI Workshop. MICCAI. http://crl.med.harvard.edu/publications/Year/2014%20Papers/Erem2014IntellMRWorkshopMICCAI.pdf. Accessed 14 June 2016