Data‐driven motion‐corrected brain <scp>MRI</scp> incorporating pose‐dependent <scp>B<sub>0</sub></scp> fields
Yannick Brackenier, Lucilio Cordero‐Grande, Raphaël Tomi‐Tricot, Tom Wilkinson, Philippa Bridgen, Anthony N. Price, Shaihan Malik, Enrico De Vita, Joseph V. Hajnal
Abstract
Purpose To 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 (B 0 ) fields. Theory and Methods Tissue susceptibility induces spatially varying B 0 distributions in the head, which change with pose. A physics‐inspired B 0 model has been deployed to model the B 0 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 B 0 variations from appropriately acquired data without requiring navigators. The effect on data‐driven motion correction is validated in simulations and in vivo. Results The applicability of the physics‐inspired B 0 model was confirmed in vivo. Simulations show the need to include the pose‐dependent B 0 fields in the reconstruction to improve motion‐correction performance and the feasibility of estimating B 0 evolution from the acquired data. The proposed motion and B 0 correction showed improved image quality for strongly corrupted data at 7 Tesla in simulations and in vivo. Conclusion We have developed a motion‐correction framework that accounts for and estimates pose‐dependent B 0 fields. The method improves current state‐of‐the‐art data‐driven motion‐correction techniques when B 0 dependencies cannot be neglected. The use of a compact physics‐inspired B 0 model together with leveraging the parallel imaging encoding redundancy and previously proposed optimized sampling patterns enables a purely data‐driven approach.