Data‐driven motion‐corrected brain MRI incorporating pose‐dependent B(0) fields

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.