ArticleMagnetic resonance in medicine2026
An Exploration of Motion-Sampling Interactions in 3D MRI for Neuroimaging.
Article in Magnetic resonance in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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Who cites it
2 citing papers in PubMed.
- View Order With Arbitrary Center Echoes.Magnetic resonance in medicine · 2026Article
- An Exploration of Motion-Sampling Interactions in 3D MRI for Neuroimaging.Magnetic resonance in medicine · 2026Article
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Authors and funding
6 authors.
Funding
Abstract
purposeTo investigate how rigid head motion interacts with 3D MRI k-space sampling strategies and to introduce motion-sampling plots as a framework for predicting motion artifacts.
methodsWe evaluated a range of motion-sampling combinations across three sampling trajectories (Cartesian, stack-of-stars, kooshball) in both simulation and in vivo. Experiments included shifting motion states in k-space, changing the direction of motion with regards to the sampling, and varying the magnitude of motion. In vivo experiments were conducted on healthy volunteers mimicking patient motion while wearing a real-time pose-tracking device. Motion-sampling plots were used to map motion states directly onto k-space and assess their relationship to artifact appearance.
resultsNine categories of motion artifacts were identified. The severity and nature of artifacts were found to depend heavily on the k-space distribution of motion states. Motion-sampling plots were seen to work as guides in predicting artifact appearance. In vivo findings supported simulation results. Artifacts were especially pronounced when motion discontinuities occurred near the center of k-space or aligned with slow phase-encoding directions.
conclusionMotion-sampling plots offer an effective way to visualize and interpret motion artifacts in 3D MRI, providing insight beyond traditional motion-time plots. This framework enables systematic evaluation of motion robustness and can guide the development and validation of motion correction techniques. We propose practical recommendations for motion experiment design to improve reproducibility and benchmarking in MRI research.
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