Data‐driven motion‐corrected brain MRI incorporating pose‐dependent B0fields

• Published in: Magnetic resonance in medicine 2022-08, Vol.88 (2), p.817-831
• Main Authors: Brackenier, Yannick, Lucilio Cordero‐Grande, Raphael Tomi‐Tricot, Wilkinson, Thomas, Bridgen, Philippa, Price, Anthony, Malik, Shaihan J, De Vita, Enrico, Hajnal, Joseph V
• Format: Article
• Language: English
• Subjects: Brain; Data acquisition; Image acquisition; Image quality; Image reconstruction; Magnetic resonance imaging; Medical imaging; Navigators; Neuroimaging; Physics; Redundancy; Simulation
• ISSN: 0740-3194; 1522-2594
• DOI: 10.1002/mrm.29255

PurposeTo develop a fully data‐driven retrospective intrascan motion‐correction framework for volumetric brain MRI at ultrahigh field (7 Tesla) that includes modeling of pose‐dependent changes in polarizing magnetic (B0) fields.Theory and MethodsTissue susceptibility induces spatially varying B0 distributions in the head, which change with pose. A physics‐inspired B0 model has been deployed to model the B0 variations in the head and was validated in vivo. This model is integrated into a forward parallel imaging model for imaging in the presence of motion. Our proposal minimizes the number of added parameters, enabling the developed framework to estimate dynamic B0 variations from appropriately acquired data without requiring navigators. The effect on data‐driven motion correction is validated in simulations and in vivo.ResultsThe applicability of the physics‐inspired B0 model was confirmed in vivo. Simulations show the need to include the pose‐dependent B0 fields in the reconstruction to improve motion‐correction performance and the feasibility of estimating B0 evolution from the acquired data. The proposed motion and B0 correction showed improved image quality for strongly corrupted data at 7 Tesla in simulations and in vivo.ConclusionWe have developed a motion‐correction framework that accounts for and estimates pose‐dependent B0 fields. The method improves current state‐of‐the‐art data‐driven motion‐correction techniques when B0 dependencies cannot be neglected. The use of a compact physics‐inspired B0 model together with leveraging the parallel imaging encoding redundancy and previously proposed optimized sampling patterns enables a purely data‐driven approach.