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Updated: Apr 25, 2026
High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain
Published on: May 10, 2012
Brain MRI Dataset Featuring a Full Clinical Protocol With and Without Intentional Motion
Kathrine Skak Madsen1, Tim Ruschke2,3, Hannah Eichhorn4,5
1Danish Research Centre for Magnetic Resonance, Department of Radiology and Nuclear Medicine, Copenhagen University Hospital - Amager and Hvidovre, Hvidovre, Denmark.
Abstract:
Motion-induced artefacts in MRI are a common occurrence but can obscure pathologies or be falsely identified as pathological. Reacquiring motion-corrupted scans is expensive, and thus retrospective and prospective motion correction methods have been introduced. Although motion correction shows promise, there is a lack of exhaustive testing on its efficacy with respect to full clinical cerebral MRI protocols. Here we present a dataset (n = 22) to facilitate future research, which includes data with and without intentional motion, and with and without prospective motion correction, across six MRI sequences included in a full clinical cerebral MRI protocol. Motion was captured by an external tracking device, and the dataset includes the motion data as derived motion transforms. For standardization, all image data are fully BIDS-compliant. Raw k-space data are available as well. As the dataset pairs motion-free data with motion-corrupted data, it can be used to develop or test different motion-correction or k-space reconstruction methods.
Insights
This study introduces a new dataset for Magnetic Resonance Imaging (MRI) motion correction research. It enables testing of advanced techniques for improving brain scan quality by reducing motion artifacts.
Area of Science:
- Medical Imaging
- Neuroimaging
- Data Science
Background:
- Motion artifacts in Magnetic Resonance Imaging (MRI) are prevalent, potentially obscuring pathologies or leading to misdiagnosis.
- Current retrospective and prospective motion correction methods show promise but require thorough validation across comprehensive clinical protocols.
- Reacquiring corrupted MRI scans is costly, highlighting the need for effective motion correction strategies.
Purpose of the Study:
- To present a novel dataset for advancing research in MRI motion correction.
- To facilitate the development and rigorous testing of motion correction and k-space reconstruction algorithms.
- To address the gap in exhaustive testing of motion correction efficacy within full clinical cerebral MRI protocols.
Main Methods:
- A dataset comprising 22 participants was created, including data with and without induced motion.
- Data were acquired across six standard MRI sequences within a clinical cerebral MRI protocol.
- Prospective motion correction was applied, and motion was tracked using an external device, with derived motion transforms included. Data are BIDS-compliant, with raw k-space data available.
Main Results:
- The dataset provides paired motion-free and motion-corrupted data for direct comparison.
- It includes comprehensive motion metadata, enabling detailed analysis of motion impact and correction effectiveness.
- The dataset is standardized and BIDS-compliant, ensuring broad usability for the research community.
Conclusions:
- This dataset serves as a valuable resource for developing and validating novel MRI motion correction techniques.
- It supports research aimed at improving the diagnostic accuracy and efficiency of cerebral MRI.
- The availability of raw k-space data further enhances its utility for advanced reconstruction method development.
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