ArticleMagnetic resonance in medicine2016
Quantitative framework for prospective motion correction evaluation.
Article in Magnetic resonance in medicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Article
- Agreement of image quality metrics with radiological evaluation in the presence of motion artifacts.Magma (New York, N.Y.) · 2025Article
- Agreement of Image Quality Metrics with Radiological Evaluation in the Presence of Motion Artifacts.ArXiv · 2025Article
- Quantifying MR head motion in the Rhineland Study - A robust method for population cohorts.NeuroImage · 2023Article
- Quantitative evaluation of prospective motion correction in healthy subjects at 7T MRI.Magnetic resonance in medicine · 2022Article
- Prospective motion correction enables highest resolution time-of-flight angiography at 7T.Magnetic resonance in medicine · 2018Article
- Prospective motion correction using coil-mounted cameras: Cross-calibration considerations.Magnetic resonance in medicine · 2018Article
- Motion correction for diffusion weighted SMS imaging.Magnetic resonance imaging · 2017Article
- Optical tracking with two markers for robust prospective motion correction for brain imaging.Magma (New York, N.Y.) · 2015Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
purposeEstablishing a framework to evaluate performances of prospective motion correction (PMC) MRI considering motion variability between MRI scans.
methodsA framework was developed to obtain quantitative comparisons between different motion correction setups, considering that varying intrinsic motion patterns between acquisitions can induce bias. Intrinsic motion was considered by replaying in a phantom experiment the recorded motion trajectories from subjects. T1-weighted MRI on five volunteers and two different marker fixations (mouth guard and nose bridge fixations) were used to test the framework. Two metrics were investigated to quantify the improvement of the image quality with PMC.
resultsMotion patterns vary between subjects as well as between repeated scans within a subject. This variability can be approximated by replaying the motion in a distinct phantom experiment and used as a covariate in models comparing motion corrections. We show that considering the intrinsic motion alters the statistical significance in comparing marker fixations. As an example, two marker fixations, a mouth guard and a nose bridge, were evaluated in terms of their effectiveness for PMC. A mouth guard achieved better PMC performance.
conclusionIntrinsic motion patterns can bias comparisons between PMC configurations and must be considered for robust evaluations. A framework for evaluating intrinsic motion patterns in PMC is presented.
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