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.

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