Brain MRI Dataset Featuring a Full Clinical Protocol With and Without Intentional Motion

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.