Paper Info
Title
Accuracy and Precision of Head Motion Information in Multi-Channel Free Induction Decay Navigators for Magnetic Resonance Imaging
Abstract
Free induction decay (FID) navigators were found to qualitatively detect rigid-body head movements, yet it is unknown to what extent they can provide quantitative motion estimates. Here, we acquired FID navigators at different sampling rates and simultaneously measured head movements using a highly accurate optical motion tracking system. This strategy allowed us to estimate the accuracy and precision of FID navigators for quantification of rigid-body head movements. Five subjects were scanned with a 32-channel head coil array on a clinical 3T MR scanner during several resting and guided head movement periods. For each subject we trained a linear regression model based on FID navigator and optical motion tracking signals. FIDbased motion model accuracy and precision was evaluated using cross-validation. FID-based prediction of rigid-body head motion was found to be with a mean translational and rotational error of 0.14±0.21 mm and 0.08±0.13°, respectively. Robust model training with sub-millimeter and sub-degree accuracy could be achieved using 100 data points with motion magnitudes of 2 mm and 1 degree for translation and rotation. The obtained linear models appeared to be subject-specific as inter-subject application of a ’universal’ FID-based motion model resulted in poor prediction accuracy. The results show that substantial rigid-body motion information is encoded in FID navigator signal time courses. Although, the applied method currently requires the simultaneous acquisition of FID signals and optical tracking data, the findings suggest that multi–channel FID navigators have a potential to complement existing tracking technologies for accurate rigid-body motion detection and correction in MRI
Year
DOI
Venue
2015
10.1109/TMI.2015.2413211
IEEE Transactions on Medical Imaging
Keywords
Field
DocType
FID navigators, magnetic resonance imaging, motion compensation, motion detection, motion estimation
Data point,Computer vision,Motion capture,Motion detection,Computer science,Linear model,Artificial intelligence,Scanner,Accuracy and precision,Free induction decay,Linear regression
Journal
Volume
Issue
ISSN
PP
99
0278-0062
Citations 
PageRank 
References 
1
0.48
5
Authors
7
Name
Order
Citations
PageRank
Maryna Babayeva110.48
Tobias Kober21379.48
Benjamin R. Knowles3393.59
Michael Herbst440.89
Reto Meuli5296107.65
Maxim Zaitsev612113.13
Gunnar Krueger715811.36